esa-sceva/satcom-chunk-collection huggingface dataset:
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 1 Introduction  
Federated learning (FL) is a new paradigm of artificial intelligence in which machines collectively learn without compromising the privacy of owner data [1]. Only model updates are shared with the central server for aggregation in FL. In contrast, the traditional machine learning (ML) approach requires all training data to be in one place. This can increase the risk of private and confidential data being leaked [2]. In the Internet era, where many of the data are online, protecting personal information has become more challenging [3, 4]. Hence, FL was proposed in [5]. Decentralized clients train their data on their own devices, and only updated gradients are sent to the server for aggregation, and the model is updated and redistributed to all clients. Thus, the server does not know the actual data. The goal of FL is to allow clients to perform ML on decentralized data without compromising their privacy [6]. Although FL promises data secrecy (because only updates are shared in the network), new security issues associated with this learning model may hinder its widespread acceptance [7, 8]. Hence, it is crucial to identify the new risks and limitations involved. For example, attackers may know the previous model, allowing them to monitor traffic and extract personal information about the end-user or leak raw data during communication. These types of attacks on personal data constitute security breaches and compromise privacy. In Figure 1, we show the likelihood of privacy breaches at various stages of FL. The Table 1 shows the symbols which will be used throughout the paper. FL uses a decentralized network that is prone to various attacks (e.g., user privacy, denial-of-service, inference, and data poisoning) [9]. Moreover, if the network is compromised, an adversary can generate a large amount of traffic, disturbing the communication between a particular client and the server, perform a man-in-the-middle attack to prevent data transmission from client to server, or generate other restrictions that prevent the model from learning. If an attacker controls the model, they can infer the training and use it for personal advantage, attempt to bias the model to promote inferences in favor of themselves, or bias the model training, therefore damaging the model. Hence, security, especially privacy, is the most crucial issue in FL. If user or data privacy is not ensured, the server can collect sensitive information via an inference attack, and private data can be reconstructed by inverting the updated gradients ([[https://www.kdnuggets.com/2020/08/breaking-privacy-federated-learning.html](https://www.kdnuggets.com/2020/08/breaking-privacy-federated-learning.html)]([https://www.kdnuggets.com/2020/08/breaking-privacy-federated-learning.html](https://www.kdnuggets.com/2020/08/breaking-privacy-federated-learning.html)), accessed
## 4 Results
### Performance Analysis by Video Type
#### 4.2.4 Thermal and Infrared Crossover (TC)  
This group is the second largest, consisting of images in which the background temperature is similar to that of the target, with approximately 53,000 images.  
DETR and GELAN demonstrate high effectiveness in this category, as they can differentiate the target despite thermal similarities with the background. This observation reinforces the capability of these models to manage challenging thermal conditions, in which other models, such as YOLO and ViTDet, exhibit increased confusion (see Table 7). Figure 7 presents an example of the results in this category.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 5 ECM-Derived Peptide Sequences and Their Use in Biomimetic Hydrogel Functionalisation
### Collagen: DGEA Motif  
DGEA is a tetrapeptide sequence found between residues 232-235 and 613-616 of the type I collagen sequence and is an \(\alpha\)2\(\beta\)1 integrin-binding site [104; 105; 106]. [PERSON] et al. [104] synthesised peptides to investigate the inhibition of platelet adhesion to collagen. It was shown that DGEA was the \(\alpha\)2\(\beta\)1 recognition sequence [104] and that integrin-collagen interaction could be disrupted by the addition of DGEA peptides, providing evidence of the importance of this sequence in \(\alpha\)2\(\beta\)1 integrin binding [107; 108].  
To investigate the DGEA motif as a biomimetic ECM peptide, alginate hydrogels were conjugated with DGEA and tested for their ability to promote cell adhesion, cell viability and osteogenic differentiation [106]. MSCs displayed limited cell adhesion and spreading on hydrogels modified with DGEA, compared with MSCs adhered to hydrogels modified with RGD [106]. These results correlated with cell viability data, where cells that were seeded onto RGD-modified hydrogels demonstrated metabolic activity that was significantly higher than MSCs on DGEA-modified hydrogels [106]. In this study, it was also hypothesised that hydrogels presenting DGEA motifs may promote osteogenic differentiation of MSCs. Indeed, all peptide-modified hydrogels including RGD, DGEA or both, showed a higher percentage of cellular alkaline phosphatase (ALP) activity than unmodified hydrogels [106]. Moreover, DGEA hydrogels stimulated the most significant collagen I and osteoclastin production compared to all other hydrogels tested [106]. Although MSCs demonstrated lower cell adhesion and viability on hydrogels modified with DGEA, their capacity for osteogenic differentiation was enhanced, suggesting that this peptide motif could be used to direct differentiation in the ECM.
## 3 Discussion of Results from Sensitivity Analyses
### Tanks-Fuselage Integration Studies Based on Part-Section Layouts  
The part section layout has the advantage of avoiding the reduction in passenger capacity, since tanks can be located under the cabin floor, although this would limit the volume available for freight and checked baggage.  
In this section, results of achievable performance refer to the data reported in Table 10, in which 2 tank groups were considered in order to increase the embarked LH\({}_{2}\).  
As Figure 21(a) shows, the maximum range is close to 3800 km which is about 65% of the reference aircraft harmonic range. Such solution, characterised by high aspect-ratio tanks (\(L/D_{str}\) = 2.5) with thin insulation layers (<50 mm), allows also to reach LH\({}_{2}\) per-par-km consumption values as low as 5 g/par/km.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Maximum tank radius (\(R\)) [m] & 2.02 \\ Tank layout & Part-section (\(y_{L}=-0.50\) m) \\ Number of tanks in the section & Variable \\ N. of tank groups (\(n_{tg}\))) & 2 \\ \(R_{min}\) [m] & 0.25 \\ Catwalk width [m] & Not present \\ \(t_{0}\) & \(-90^{\circ}\) \\ \(t_{ins}\) [mm] & Variable \\ \(L/D_{str}\) & Variable \\ \(D_{str}\) [m] & Variable \\ \(d_{supp}/L\) & 0.80 \\ \(f_{end}\) & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Data on the reference multi tank/part-section configuration (cargo deck).  
Figure 22: Part-section layout (cargo deck) with 2 groups of tanks: (a) sensitivity of burnt LH\({}_{2}\) per-par-km to \(L/D_{str}\) and \(t_{ins}\), (b) total length of tanks (2 groups).  
In addition, the total length of both the tanks' groups is close to 10 m (Figure 22b), which appears to be compatible with the fuselage dimensions. The white areas in Figure 22 refer to cases in which the tank volume is not sufficient to complete the mission.
## 2 Reverse Logistics Facility Location Model
### Target Function of Considering Carbon Emission  
#### 2.2.1 Carbon Emission Cost Calculation  
The purpose of reverse logistics is to save energy and reduce emissions. In the research boom of the low-carbon economy and green logistics, greenhouse gas emissions have always been the focus of attention [17; 18; 19], and the setting of logistics nodes is closely related to carbon emissions. How to integrate the idea of emission reduction into the system design and build a green logistics network plays a practical role. Some results have been achieved by taking carbon emissions as a function parameter and participating in the optimization process with a quantitative method [20; 21; 22; 23; 24]. Considering the carbon emission sources of a reverse logistics network, they are mainly divided into transport carbon emission and carbon emission processing [25].  
The carbon emission in logistics transportation includes the indirect or direct carbon dioxide emission from various substances and vehicles [26]. According to the theory of energy consumption, vehicle carbon emissions are related to route slope, body weight and route length [27; 28; 29]. There are obvious uncertainties between people and the road environment, which are treated as deterministic factors in this paper. When the vehicle itself is used as a means of transport, it is purchased uniformly by the company, and the factors affecting vehicle carbon emissions such as engine and shape have also been fixed. Therefore, as long as carbon emissions are under fixed conditions, considering vehicle fuel consumption is directly linked to vehicle carbon emission quantification. Therefore, it can be summarized as: carbon emissions = fuel consumption * \(CO_{2}\) emission coefficient [30]. Therefore, transport carbon emissions can be quantified by the carbon emission coefficient of recycled products per unit distance per unit weight [31]. To be specific, the transportation is divided into two stages: recycling center to sorting center and sorting center to processing center, and the carbon emissions are represented by distance and transportation volume.  
Waste products also produce carbon emissions in the processing process, and for different processing processes, carbon emissions are different. The product treatment process can be divided into classification treatment at the detection classification center, secondary renovation treatment in recycling treatment, recyclable parts dismantling in recycling treatment, fine dismantling in recycling treatment and waste treatment. According to the different process carbon emission standards [32; 33], the carbon emission of product recycling process can be obtained.
# A Hybrid Adversarial Attack for Different Application Scenarios +
## 4 Experiments
### Evaluation
#### 4.2.3 Human Evaluation  
In order to illustrate that adversarial text with more dispersion words modification could lead to being more similar to the original text and more imperceptible for humans, we select five volunteers to score the similarity between the adversarial examples and the original text from 1 to 5. We randomly chose 10 comments with positive and negative examples and their adversarial examples generated by greedy search, global search, and the global search with CV. This is used to prove the similarity between the original text and adversarial examples. On the other hand, we select 15 comments including the original text, greedy text, global text, and global text with CV, and then ask volunteers to identify which of them are modified adversarial examples. This is used to prove the imperceptibility for humans. After counting all the scoring results, in terms of similarity, we calculate the average score, the global text with CV similarity score is 3.642, the global text score is 3.612, and the greedy text score is 2.91. Because the greedy text often modifies the front words by a large amount, it can obviously find the difference from the original text. In terms of imperceptibility, the global text with CV has a detectable ratio of 0.37, the global text has a detectable ratio of 0.4, and the greedy text has a detectable ratio of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\lambda\) & **0** & **1** & **5** & **10** & **20** & **30** \\ \hline CV value & 0.487 & 0.589 & 0.70 & 0.73 & 0.827 & 0.886 \\ attack rate & 77.4\% & 77.6\% & 76.6\% & 76.8\% & 77\% & 76.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The attack success rate and CV value under different \(\lambda\).  
Figure 2: The total success rate of greedy attack and global attack added cosine distance.  
0.46. We find that the scores of global attack and global attack with CV are close, and they are better than greedy attacks.
## 2 Materials and Methods  
The FAPbI\({}_{3}\) PQDs were synthesized according to the recent report [25]. For fabrication of the PTAA/FAPbI\({}_{3}\)/TiO\({}_{2}\) sample, a 50 nm compact hydrothermal TiO\({}_{2}\) layer was deposited above the quartz substrate. The TiO\({}_{2}\) film was exposed to UV-ozone for 10 min before FAPbI\({}_{3}\) PQDs deposition. The FAPbI\({}_{3}\) PQDs (45 mg/mL in chloroform) were then spin-cast on top of as-prepared TiO\({}_{2}\) film at 2000 rpm for 40 s. Then the PQDs film was swiftly treated by 200 \(\upmu\)L MeOAc for 5 s to remove the surface ligands and spun at 2000 rpm for 20 s. The PQDs film deposition process, performed in the dry air-filled glovebox with relative humidity below 10%, could build up a \(\sim\)160 nm thick film. Before the fabrication of HTL, the PQDs film was annealed at 70 \({}^{\circ}\)C for 7 min in a nitrogen-filled glovebox, which can further remove the solvent. By spin-coating the tris(pentafluorophenyl)borane-doped PTAA toluene solution (15 mg/mL, dopant/PTAA = 5% in weight) at 3000 rpm for 40 s, the 40 nm HTL could be deposited above the FAPbI\({}_{3}\) PQDs layer in the N\({}_{2}\) glovebox.  
We used a Perkin Elmer model Lambda 950 spectrophotometer to characterize the ultraviolet-visible (UV-vis) absorption spectra of the FAPbI\({}_{3}\) PQDs. Their photoluminescence (PL) spectra were measured with a FluoroMax-4 spectrofluorometer (HORIBA Scientific). The transmission electron microscopy (TEM) images were obtained by using Tecnai G2 F20 S-Twin transmission electron microscope.  
The TAs were measured by using a pump\(-\)probe experimental system. The pump and probe pulses were delivered by a Ti:sapphire amplifier laser with the pulse width of 100 fs and repetition rate of 1 kHz. We used a mechanical chopper to modulate the pump beam at \(\sim\)300 Hz and focused the laser on the sample with a spot diameter of \(\sim\)3 mm. The pump beam, converted from the fundamental laser beam by using of a \(\beta\)-barium borate (BBO) crystal, has the central wavelength of 400 nm. Supercontinuum white light was generated by a sapphire plate irradiated by 800 nm pulses and it then passed though different color filters with 10 nm bandwidth to serve as the probe pulses with the wavelength range from 500 to 1000 nm. A silicon photodiode was used to collect the probe light transmitted through the sample. A lock-in amplifier was connected to the photodiode to extract the transmission change (\(\Delta T\)) between the states with and without the pump excitation.
## 3 Results and Discussion  
### Analysis of the Dry Mycelium Response and Model Building  
During the agitated incubation, one of the experimental variants was lost due to a mechanical impact within the incubator. Modeling of the dry weight response proceeded in the absence of this variant, without issue.  
After the mycelia were lyophilized and weighed, each broth variant was assigned with the experimentally obtained weight values. Design-Expert software was used to carry out an analysis of the dry weight response and build a model of the interactions between the design factors and the response. This model was then able to generate predictions about the response for any given combination of factors within the design constraints. For the actual experimental runs, the model was also able to predict theoretical yields (Table 3).  
To better illustrate the relationship between the first two terms of this table, their values were plotted together and color-coded by dry weight (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Run Order**} & **Actual Value** & **Predicted Value** & **Residual** & **Leverage** & **Internally** & **Externally** & **Cook’s** \\  & **(g/500 mL)** & **(g/500 mL)** & **Residual** & **Leverage** & **Studentized** & **Residuals** & **Distance** \\ \hline
2 & 0.8200 & 0.7275 & 0.0925 & 0.696 & 0.647 & 0.634 & 0.068 \\ \hline
3 & 1.16 & 1.15 & 0.0052 & 0.577 & 0.031 & 0.0  
An analysis of variance between the predicted and actual dry mycelium weights was carried out for two-component interactions and for every factor (Table 4) to determine whether the response modeling was statistically significant and could reasonably predict an optimal broth composition.  
The model F-value of 2.76 indicates the model is significant. There is only a 3.16% chance that an F-value this large can occur due to noise. The \(p\)-value of 0.0316 also indicates that the model is significant.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline Model & 2.42 & 13 & 0.1860 & 2.76 & 0.0316 \\ \hline \({}^{(1)}\) Linear Mixture & 0.0752 & 3 & 0.0251 & 0.3721 & 0.7743 \\ \hline AB & 0.1874 & 1 & 0.1874 & 2.78 & 0.1161 \\ \hline AC & 0.0859 & 1 & 0.0859 & 1.28 & 0.2764 \\ \hline AD & 0.1174 & 1 & 0.1174 & 1.74 & 0.2066 \\ \hline AG & 0.0070 & 1 & 0.0070 & 0.1038 & 0.7518 \\ \hline BC & 0.0356 & 1 & 0.0356 & 0.5291 & 0.4782 \\ \hline BD & 0.0244 & 1 & 0.0244 & 0.3629 & 0.5559 \\ \hline BG & 0.4574 & 1 & 0.4574 & 6.79 & 0.0199 \\ \hline CD & 0.2314 & 1 & 0.2314 & 3.44 & 0.0836 \\ \hline CG & 0.0031 & 1 & 0.0031 & 0.0456 & 0.8338 \\ \hline DG & 0.0075 & 1 & 0.0075 & 0.1108 & 0.7439 \\ \hline Residual & 1.01 & 15 & 0.0674 & & \\ \hline Cor Total & 3.43 & 28 & & & \\ \hline \end{tabular} \({}^{(1)}\) Inference for linear mixtures uses Type I sums of squares.  
\end{table}
Table 4: ANOVA for reduced quadratic \(\times\) linear modeling of dry mycelium response.  
Figure 3: The predicted against actual results of dry mycelium weight (g/500 mL).
## 4 The Establishment of Networks
### Control Network  
The main function of the control network is to solve the conflict of \(\Sigma_{mnt}\). Therefore, the control network needs to add a set of selection functions \(Pr\) on the basis of \(\Sigma_{mnt}\). The relevant definitions are as follows.  
Definition 2: For \(\forall p_{i}\in P\), \(|p_{i}^{\bullet}|>1\to p_{i}\in P_{cfl}\). On the contrary, \(|p_{i}^{\bullet}|\leq 1\to p_{i}\in P_{ncfl}\). Among them, \(P_{cfl}\) is the conflict place set, and \(P_{ncfl}\) is the non-conflict place set.  
Definition 3: Pr: \(P_{c}\to T\). If \(p_{i}\in P_{c}\), \(Pr(p_{i})\) means that the conflict selection is made at the location of the conflict place \(p_{i}\), and the result of the selection is no longer random, but \(t_{j}\in p_{i}^{\bullet}\) is enabled after being constrained by the function \(Pr\).  
Definition 4: The _EDSPN_ model \(\Sigma_{ct}\) of the control network is defined as: \[\Sigma_{ct}\text{=}(\Sigma_{mnt},Pr).\] (4)  
**Definition 5**.: _The workpiece belongs to the individual who flows in the net and is represented as a token in Petri net. Thus, define the token set as \(D\) and individual token \(d\in D\). Further, define the following forms: \(d\odot p\) indicates that token \(d\) is in place \(p\). Tokens have attributes in the form:_  
\[d(type,item_{1},item_{2},\ldots,item_{n},n\in\mathbb{N}^{+}). \tag{5}\]  
_where \(d.type\) represents the artifact type, \(d.type.item\) represents other attributes of the artifact, and \(n\) is the order of concerned attributes of the workpiece, which is limited._  
**Definition 6**.: _As illustrated in Figure 7, define working group in the form of class, which is a virtual class that has its own properties and methods, such as \(Group(t_{i})\) means to retrieve the working group instance where \(t_{i}\) is located and return it. Define the mathematical form of the working group as:_  
\[Group(StateAndOperate,Operator,Machine,\ldots). \tag{6}\]  
Among them, \"\(\ldots\)\" indicates the class members that are dispensable to readers, while not means infinity, and Figure 7 is the same.  
The following is the method of establishing a control network part:  
1. Get the mapping of set places, transitions and tokens from \(\Sigma_{mult}\). At the same time, a variable table \(X_{HMI}\) in the human-machine interaction component of DView is maintained. This table mainly provides temporary variables during control as well as variables that need to be displayed in the interface but do not need to exist in the PLC.
2. Select the conflict places \(p_{i}\in P_{cfl}\) and set \(Pr(p_{i})\). In order to allow tokens to choose the expected transition flow, the function of \(Pr(p_{i})\) can be the choice of station, product quality decision, etc.
3. For a fully automated network, set the control options for the transition, such as start and stop, to prevent the workpiece from being processed into scrap, etc., when an exception occurs.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
###### Abstract  
Low Earth Orbit (LEO) constellations have recently gained tremendous attention in the navigational field due to their larger constellation size, faster geometry variations, and higher signal power levels than Global Navigation Satellite Systems (GNSS), making them favourable for Position, Navigation, and Timing (PNT) purposes. Satellite signals are heavily attenuated from the atmospheric layers, especially from the ionosphere. Ionospheric delays are, however, expected to be smaller in signals from LEO satellites than GNSS due to their lower orbital altitudes and higher carrier frequency. Nevertheless, unlike for GNSS, there are currently no standardized models for correcting the ionospheric errors in LEO signals. In this paper, we derive a new model called Interpolated and Averaged Memory Model (IAMM) starting from existing International GNSS Service (IGS) data and based on the observation that ionospheric effects repeat every 11 years. Our IAMM model can be used for ionospheric corrections for signals from any satellite constellation, including LEO. This model is constructed based on averaging multiple ionospheric data and reflecting the electron content inside the ionosphere. The IAMM model's primary advantage is its ability to be used both online and offline without needing real-time input parameters, thus making it easy to store in a device's memory. We compare this model with two benchmark models, the Klobuchar and International Reference Ionosphere (IRI) models, by utilizing GNSS measurement data from 24 scenarios acquired in several European countries using both professional GNSS receivers and Android smartphones. The model's behaviour is also evaluated on LEO signals using simulated data (as measurement data based on LEO signals are still not available in the open-access community; we show a significant reduction in ionospheric delays in LEO signals compared to GNSS. Finally, we highlight the remaining open challenges toward viable ionospheric-delay models in an LEO-PNT context.  
Keywords:Ionosphere; ionospheric delay model; Interpolated and Averaged Memory Model; Global Navigation Satellite Systems (GNSS); Low Earth Orbit (LEO); LEO-Position, Navigation, and Timing (LEO-PNT); satellite; Android raw measurements; GNSS logger; Total Electron Content (TEC) +
Footnote †: journal: _Computers_