esa-sceva/satcom-chunk-collection huggingface dataset:
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 7 Conclusions  
In this paper, we introduced an innovative approach to detect relay attacks on time-stable networks. Our protocol is based on a distance-bounding mechanism that detects abnormally long round-trip times. To the best of our knowledge, our solution is the first time-based hijack detection over the internet. We experimentally show that our protocol is able to detect all deviated traffic up to realistic attacker capabilities, and we offer users the possibility to control the sensitivity of the decision process. This sensitivity control allows an absolute 0% of false positives while leaving realistically low chances to perform a successful relay in the long run. We studied the long-term behavior of RTT over the Internet for UDP packets and observed satisfying stability, allowing the reference sample not to be updated too frequently. We showed that our protocol adds absolutely no workload for the forwarding intermediary equipment and demands very low computational capabilities for the endpoints while keeping very satisfying throughput performances. Finally, we described what we believe to be two realistic applications of our protocol where both parties can benefit from its use. It is worth noting that our protocol can be easily deployed:application-oriented, it does not require any update of the routers, it can be used by partners without following a long standardization process and does not require to be updated when the routing process changes as long as the RTT conserves a satisfying stability.  
Conceptualization, O.G. and G.A. ([PERSON]) and G.A. ([PERSON]) and J.T.; investigation, O.G.; data curation, O.G.; methodology, O.G. and G.A. ([PERSON]) and G.A. ([PERSON]) and J.T.; software, O.G.; writing--original draft preparation, O.G.; writing--review and editing, G.A. ([PERSON]) and G.A. ([PERSON]) and J.T.; supervision, G.A. ([PERSON]) and G.A. ([PERSON]) and J.T. All authors have read and agreed to the submitted version of the manuscript.  
The research leading to these results partly received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 871808 (SGPPP project INSPIRE-5 Gplus). The article reflects only the authors' views. The Commission is not responsible for any use that may be made of the information it contains.  
All the measures supporting this work can be found at [[https://github.com/[PERSON]/ICRP.git](https://github.com/[PERSON]/ICRP.git)]([https://github.com/[PERSON]/ICRP.git](https://github.com/[PERSON]/ICRP.git)), accessed on 29 August 2022.  
Authors want to thank the support of Orange Labs Services, Poland which supplied virtual nodes to extend the experiments to a larger scale.  
The authors declare the following conflict of interest: [PERSON] is a member of the editorial board of the journal Cryptography.
## 3 Background
### EdgeSimPy Limitations  
Although simulators are very useful for evaluating algorithm performance in edge computing settings, there are a number of drawbacks and difficulties that researchers need to take into account. The natural abstraction and simplification of real-world situations are some of the main drawbacks, which might cause differences between simulated outcomes and actual performance in real deployments. The heterogeneity and dynamic nature of edge settings, which include variations in device capabilities, network circumstances, and workload patterns, are typically difficult for simulators to accurately simulate. Performance reviews may become unduly optimistic or pessimistic as a result. Furthermore, the subtle differences in energy usage between various edge devices may not be adequately captured by simulators, resulting in ratings of energy efficiency that are less accurate. The potential lack of scalability in simulation tools is another difficulty; they may not be able to manage intricate, large-scale edge networks or faithfully replicate interactions between several devices. Additionally, simulators frequently lack the fidelity of security and privacy issues,  
Figure 3: EdgeSimPy simulation workflow [3].  
which could lead to the neglect of important facets of edge computing data protection. To obtain more thorough and reliable assessments of their algorithms, researchers need to be conscious of these limitations and supplement simulation studies with in-person trials or hybrid methodologies.
# Exploring New Fruit- and Vegetable-Derived Rennet for Cheese Making
## 2 Materials and Methods
### Plant, Fruit, and Mushroom Sources and Their Extraction  
Artichokes (_Cynara carduculus_ var. _scolymus_), cardoons (_Cynara carduculus_), and _Ficus carica_ were cultivated without the use of chemicals and pesticides in an agricultural zone of the Acerra city (near Naples, Italy). Pineapple, papaya, and the fungus _Pleurotus ostreatus_ were bought from organic crops. Plant, fruit, and mushroom samples were collected during their expected maturation time. Internal bracts of artichokes, whole oyster mushroom and cardoons, and the pulp of pineapple and papaya were utilized. The fresh materials were dried under a stream of air at 45 \({}^{\circ}\)C and stored at 4 \({}^{\circ}\)C until their use. Fig milky sap was taken and immediately used in mini-curd making.  
The extracts were prepared following the procedure described by [PERSON] et al. [4], with some modifications. The dried materials (10 g for each considered source) were incubated in 30 mL of tap water for 6 h under stirring at room temperature. After incubation, the supernatants were recovered by centrifugation (10 min at 9800\(\times\)\(g\), 4 \({}^{\circ}\)C) and freshly used for assays and mini-curd making. Fresh extracts were evaluated for their color and smell (see Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Calf** & **Internal Bracts** & **Cardoon** & **Pineapple** & **Papaya** & **Fig Milky Sap** & **Oyster** & **Mushroom** \\ \hline pH & 5.5 & 5.8 & 5.5 & 6.2 & 5.3 & 5.0 & 5.5 \\ \hline esterase & & & & & & & \\ activity (U/mL) & 0.017 \(\pm\) 0.001 & 0.010 \(\pm\) 0.001 & 0.008 \(\pm\) 0.001 & 0.013 \(\pm\) 0.002 & 0.004 \(\pm\) 0.001 & 0.012 \(\pm\) 0.001 & 0.016 \(\pm\) 0.002 \\ \hline protease activity [5] & & & & & & & \\ (\% BSA digested) & 46 \(\pm\) 4 & \(>\)99 & \(>\)99 & 28 \(\pm\) 5 & 18 \(\pm\) 4 & 51 \(\pm\) 4 & 98 \(\pm\) 2 \\ \hline MCA (SU \({}^{\circ}\)) & 8000 & 800 & 667 & 1067 & 593 & 8000 & 640 \\ \hline fresh c.p.* color & straw yellow & light greenish & light greenish & straw yellow & yellow & white & straw yellow \\ \hline fresh c.p.* color & fresh cheese & artichoke & hay & n.d. & n.d. & fig & fresh mushrooms \\ \hline c.p.* yield from 500 & 72 \(\pm\) 5 & 68 \(\pm\) 3 & 70 \(\pm\) 4 & 57 \(\pm\) 3 & 67 \(\pm\) 4 & 69 \(\pm\) 3 & 51 \(\pm\) 4 \\ \hline  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \end{tabular}
\end{table}
Table 1: Main characteristics of the vegetable and fruit rennet. The extract from vegetables and fruits were obtained in tap water starting from dried material. The extracts were tested for their pH, esterase activity, and protease activity (% of digested BSA). Milk-clotting activity (MCA) was determined and indicated as Standard Units (SUs); see Materials and Methods section. The fresh cheese curds obtained were weighted and observed for their color and odor. The yield of cheese curds was determined considering the amount obtained starting from 500 mL of milk.
## 4 Materials and Methods
### SDS/PAGE, Circular Dichroism and LC/ESI Mass Spectrometry
#### 4.5.2 Circular Dichroism  
The far-UV circular dichroism (CD) spectra (from 204 to 260 nm) was collected on a JACSO J-810 CD spectrophotometer. LAIR-1 variants samples at a concentration of 4 \(\upmu\)M in 5 mM Trizma, 150 mM NaF (pH 8.2) were scanned 10 times at 20 \({}^{\circ}\)C in a 1 mm path-length cuvette. After subtraction of baseline buffer values, the CD signal was converted to mean molar residue ellipticity (deg.cm\({}^{2}\).dmol\({}^{-1}\)).
## 2 Theoretical Analysis
The introduction of the tilt leads to an increase in the total loss L0\({}_{0n}\) of each mode in the cavity, but the increase in the loss of the higher-order mode is less than that of the lower-order mode. For a given aperture, there is a suitable tilt angle that prevents the lower-order modes from oscillating, and the highest-order mode HG0\({}_{0N}\) wins in the mode competition. However, when the pump power is high, each order mode in the resonator can obtain sufficient gain, G0\({}_{0n}\), which causes the simultaneous resolution of each mode in the cavity, producing a multimode laser output. Therefore, in order to achieve specific high-order mode output, the pump power needs to be controlled at a reasonable level. Thus, their output power is limited for a single oscillator. The master oscillator power amplifier (MOPA) effectively scales the output power of high-purity HG0\({}_{0n}\) laser beams. The slab with a large aspect ratio used in the amplifier provides an ideal condition for power scaling of strip-shaped HG0\({}_{0n}\) laser beams without additional beam shaping. Another advantage of using a slab as an amplification gain medium is the simplicity of thermal management. Since the zig-zag optical path in the slab averages the thermal gradient, there is almost no first-order thermal lens effect, making it a promising method for scaling laser output to higher power while simultaneously maintaining good beam quality.
###### Abstract
One of the main difference is that the difficulty caused from the high rate time-varying parameters can be resolved by utilizing compensative parameters, while the previous FSMC can work for only the slow rate time-varying parameters. In addition, the calculating cost of the estimating parameters is less than that of the previous FSMC. (2) In the design process, the UAD is separated into two groups related to uncertainties and external disturbances in order to deal with them individually. The first group is estimated by the fuzzy structure of the FSMC, while the second by the DE. Subsequently, an adaptive control law based on Lyapunov stability analysis is formulated to update the parameters of the DE-FSMC so that both the closed-loop stability and the finite-time convergence of the tracking error are guaranteed. (3) In order to validate the effectiveness of the proposed control method, an experimental investigation is undertaken by adopting a semi-active vehicle suspension system installed with magnetorheological damper (MRD). Then, a comparative work between the proposed control method and two existing fuzzy sliding mode controllers is undertaken.
# The SXFEL Upgrade: From Test Facility to User Facility
## 3 Various Types of Undulators for SXFEL-UF  
The SXFEL undulator parameters for seeding line and SASE line are listed in Table 3. Variable gap is realized with four independent servo motors for all the undulators. The challenges during fabrication include mass production, precise alignment, and synchronized motion. The SASE line mainly consists of ten in-vacuum undulators with 16 mm period, whereas the seeding line consists of ten U23.5 with period 23.5 mm, six U30 with period 30 mm and two elliptical polarization undulators EPU30, as shown in Figure 13. C-type mechanical frame has been adopted for all undulators. The motion control is based on Beckhoff PLC system and the gap measurement is achieved from absolute linear encoders. All electrical control boxes are fixed at the undulator support structure and therefore installed in the undulator tunnel. An IVU16 magnet assembly is mounted onto an extruded aluminum girder inside a two-side open-able vacuum chamber, thus magnetic performance of IVUs can be optimized with high precision hall probe even after the magnets have been put inside vacuum chamber. After magnetic field optimization at different gaps, the magnetic axis can be transferred to mechanical fiducial marks by a laser tracker system.  
Figure 12: Comparison between a single-stage EEHG and EEHG-HGHG cascade with the wavelength of about 3 nm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**SASE**} & \multicolumn{2}{c}{**Seeding**} \\ \hline Model & IVU16 & EPU20 & U23.5 & U30/50 & EPU30 \\ \hline Period Length (mm) & 16 & 20 & 23.5 & 30/50 & 30 \\ \hline Min. gap (mm) & 3.8 & 6.7 & 8.75 & 9 & 9.3 \\ \hline Effective field (T) & 1.14 & 0.8 & 0.65 & 0.8/1.18 & 0.8 \\ \hline Magnet & SmCo Br = 1.13 T & NdFeB Br = 1.3 T & NdFeB Br = 1.25 T & NdFeB Br = 1.25 T & NdFeB Br = 1.25 T \\ \hline Magnet Structure & IVU-Hybrid & APPLE-III & Hybrid & Hybrid: switchable & APPLE-II \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of SXFEL undulators.
## 3 Results and Discussion
### Volatile Profiles of Vidal Grapes in Weixi Region after On-Vine Dehydration (T5)
#### 3.2.3 IPDs  
The percentage of IPDs in the total content of volatile compounds was 12.45%, where the percentages of free and bound forms of volatiles were 6.33% and 15.18%, respectively (Figure 2). Twenty-five monoterponids and four norisoprenoids were detected in Vidal grapes, whose percentages in the total IPDs were 99.33% and 0.67%, respectively. The IPDs were present mainly in glycosidically bound form, which was 5.41 times higher than the free form, accounting for 84.40% of the total IPDs.  
Among the monoterpenes, the most abundant were geraniol, linalool, \(\beta\)-_trans_-ocimene, and \(\beta\)-myrcene, which accounted for 62.14% of the total monoterpene content (free + bound) at 30.52%, 10.24%, 11.06%, and 10.32%, respectively. Similar to Muscat varieties [2], linalool and geraniol were the most important monoterpenes in Vidal grapes from the Weixi region, where both concentrations of free forms were above their odor threshold. The total content of free and bound forms of linalool and geraniol reached 18.51 and 8.27 times their thresholds, respectively, which means that they would contribute directly to the final wine aroma, with hyacinth, rose, and citrus notes [2]. Although _cis_-rose oxide was not present in high concentrations, it is another powerful aromatic compound for Vidal grapes, due to its low threshold (0.1-0.5 ug/L), as described in Gewurztraminer, Muscat, Traminette, and Riesling grapes [2, 28].  
Four norisoprenoids were detected in Vidal grapes, of which \(\beta\)-damascenone was undoubtedly the most essential. Contrary to most terpen, the free form of \(\beta\)-damascenone was 5.93 times more than that of the bound form--a level that is 58.80 times its threshold in water (0.01 ug/L [29]). Some authors have also found that \(\beta\)-damascenone accumulated during the late-harvest dehydration process of Shiraz grapes [30] and the storing process of raising [15].
# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 3 Problem Description and Model Design
### Improved Informer Modeling
#### Improving Supply Forecasting with the Informer Model  
This paper addresses the issue of supply volume forecasting by first constructing a multidimensional feature engineering framework aimed at extracting key features from various dimensions to enhance the expressiveness of label data. This involves capturing features related to spatiotemporal evolution characteristics, freight rates, and demand volume, thereby comprehensively revealing the intrinsic properties of the data and improving the model's interpretability of the label data. Next, an embedding layer is introduced to facilitate effective encoding of data features, providing rich input information for subsequent model learning. Additionally, the STCN module is designed to further extract the spatiotemporal features of the supply volume data, considering dynamic information across time and space. Finally, an LSTM model is incorporated to capture both short-term and long-term dependencies in the supply volume, enhancing the model's ability to grasp long-range dependencies. The prediction values are subsequently obtained through a multilayer perceptron. Overall, the structure of the improved Informer model is illustrated in Figure 5.  
Figure 4: LSTM block.
# Construction of IsoVoc Database for the Authentication of Natural Flavours
## 3 Results and Discussion
### Selection of Authentic Reference Sample
We also observed some differences in isotope values between varieties (such as in red delicious for apples) and samples from different geographical locations (samples from Poland and Japan differentiate from Slovenian ones) that could be important when understanding isotope fractionation rates. However, we cannot draw any general conclusion on this subject since we are dealing with a limited number of samples, and further investigation in this field is required. Importantly, the following question arises: can fresh fruit samples substitute distillates in the database? For all aroma compounds where the \(\delta^{13}\)C values are within the min and max value of the distillates, this is possible, as in the case of 1-hexanol, (E)-2-hexen-1-ol, 2-methylbutyl acetate, ethyl butyrate, and propyl acetate for apples and 1-hexanol, ethyl butyrate, hexyl acetate, methyl butyrate, and methyl hexanoate for strawberries. That means that an authenticity assessment will not be influenced by this effect. However, if we want to cover more fruit VOCs, we need to include both distillates and fruit samples in the database. Next, for apple and strawberry, distillates and fruits were first combined into a single group (Figure 3). Finally, we compared the data with that for raspberries, blueberries, peaches, pears, and sour cherries to find out which aroma compounds are common to the selected fruit types and which occur most frequently. We then evaluated the \(\delta^{13}\)C values of 1-hexanol, (E)-2-hexen-1-ol, (E)-2-hexenal, benzaldehyde, butyl acetate, ethyl lactate, ethyl butyrate, ethyl hexanoate, hexanal, and hexyl acetate. We also found that apples and strawberries account for most of the most variability in the natural range of \(\delta^{13}\)C values and contain the highest number of VOCs, which makes them the most appropriate fruits for database creation. Figure 2: Comparison of \(\delta^{13}\)C values of VOCs present in strawberry fruit and distillate. \({}^{*}\)\(p<0.05\) (Mann-Whitney test).
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 3 Proposed Methodology: DCBA Fault Diagnosis
### Integrated Diagnosis Model
#### 3.2.2 CNN Layer  
A convolutional neural network (CNN) layer consists of two main components: the convolution operation and the activation operation. The input to the convolution operation is a three-dimensional data structure \(X\in R^{w\times h\times c}\), where the dimensions represent the width, height, and number of channels of the input data. Specifically, when \(c=1\), the  
Figure 4: Schematic diagram of sliding window processing.  
Figure 3: Proposed integrated diagnosis model CNN-BLSTM-attention.  
three-dimensional input is transformed into a two-dimensional data format, which serves as the input to the model. Specifically, the horizontal axis represents the feature vectors, and the vertical axis represents the time series, determined by the feature vectors' input and the width of the sliding window.  
To further elaborate on the convolution operation, we first define four hyperparameters: the number of convolution kernels \(K\), the size of the convolution kernels \(F\) (\(fw\times fh\)), the stride of the convolution S, and the amount of padding \(P\). For each convolution kernel, the input datum \(X\in R^{w\times h\times c}\) is padded with \(P\) to produce the output \(\overline{X}\in R^{(w+P)\times(h+P)\times c}\), and then sliced according to the size of the convolution kernel. Each slice is element-wise multiplied with the kernel and accumulated to produce the output \(\hat{X}\) of the convolution operation. The width and height of the output can be determined as follows:  
\[\hat{w}=\frac{w-F+2P}{S}+1, \tag{1}\]  
and  
\[\hat{h}=\frac{h-F+2P}{S}+1. \tag{2}\]  
In the convolution operation, each convolution kernel produces one channel of output. With \(K\) convolution kernels, a new three-dimensional data structure is obtained. The convolution process is illustrated in Figure 5.  
After convolutional operations, it is necessary to apply an activation function to the output. The purpose of the activation function is to introduce non-linear expressive power, enabling the model to learn more complex feature representations and enhancing its learning capability. A common activation function is the Rectified Linear Unit (ReLU), which activates each element in the output of the convolutional operation as follows:  
\[ReLU(\hat{X})=max(\hat{X}[i,j,k],0), \tag{3}\]  
Here, \(\hat{X}[i,j,k]\) represents each element in the output of the convolution operation.
## 5 Experiments
### Scenarios  
Application of proposed algorithms to periodic sensor networks involves computation of a set of frames coordinating node activity, allowing all nodes to send their data to the base node. An appropriate scenario consisting of a selected number of frames was generated by randomly drawing indexes of nodes having data to send to provide realistic results for each network. There was an equal number of data sending nodes in every frame when the number of nodes was fully divisible by the number of slots. Otherwise, it was accepted that the last scenario served fewer nodes--see Figure 3 for an example of such a scenario.  
It must be noted that the base station does not send data, so the number of data sending nodes is \(N-1\). When an equal number of data sending nodes was required, the additional nodes were randomly duplicated. Square grid topology was adopted to make tests synthetic and easy to interpret. The node can operate in one of five states: sleeping, receiving and transmitting with low, medium or high power settings. The corresponding ranges and power consumption are presented in Table 2.  
The radio characteristics of the nodes are based on Texas Instruments CC2420 [31] chip used commonly in sensor nodes, e.g., in TelosB family. The ranges shown in Table 2 are used to infer model parameters \(A_{ik}^{j}\) and \(B_{ik}^{j}\) while power levels are used as values of  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline  & \multicolumn{2}{c}{**Energy State**} & \multicolumn{2}{c}{**Transmission Range [m]**} & \multicolumn{1}{l}{**Power**} \\ \cline{3-5}
**Number** & **Description** & **Effective** & **Interference** & **[mW]** \\ \hline
1 & sleeping & – & – & 0.004 \\ \hline
2 & receiving & – & – & 120 \\ \hline
3 & transmitting—low & 65 & 85 & 85 \\ \hline
4 & transmitting—medium & 95 & 115 & 98 \\ \hline
5 & transmitting—high & 125 & 145 & 112 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Energy states and ranges of sensor nodes used in experiments.  
parameter \(\phi_{k}\) to calculate the performance index. The grid edge length was set at 50 m, so starting from a 3 \(\times\) 3 grid, at least some of the nodes require multi-hop communication to reach the base node.  
The experiments were carried out using scripts written in Perl language driving input data preparation and output data collection for CPLEX solver (version 20.1.0.0). The mathematical programming model and data were encoded using AMPL language. The machine used for computations was equipped with an Intel Xeon E3-1240 v.3 processor with eight cores operating at 3.4 GHz and 8 GB RAM.