esa-sceva/satcom-chunk-collection huggingface dataset:
# Random Access Performance of Distributed Sensors Attacked by Unknown Jammers
## 3 RA Performance Analysis
### Throughput of Random Access Request  
Let \(q_{I}\) denote the probability that a jammer receives AI successfully. The success probability of MT after the successful \(r\)-th preamble retransmission in the presence of the power jamming and the code jamming for given \(n_{m}\), \(n_{m,t}\), \(n_{p,t}\) and \(n_{I}\) can be derived as:  
\[T_{P}(r|n_{m},n_{m,t},n_{p,t},n_{I})=\sum_{n_{p}=1}^{\infty}\sum _{k=1}^{n_{p}}\Pr\{K=k|N=n_{p}\}\Pr\{N=n_{p}\}u_{P}(r|n_{p},k,n_{m},n_{I})q_{A}\] \[\times\sum_{w=0}^{k-1}{k-1\choose w}q^{w}(1-q)^{k-1-w}\bigg{[} \underbrace{\mathcal{P}_{A}^{(\mathrm{wco})}}_{(\mathrm{I})}\underbrace{\Pr\{ \gamma_{A}^{(m)}\geq\beta_{m}|n_{m,t},n_{p,t},w,n_{I}\}}_{(\mathrm{II})}\bigg{]}, \tag{18}\]  
and:  
\[T_{\mathrm{C}}(r|n_{m},n_{m,t},n_{p,t},n_{I}) =\sum_{n_{p}=1}^{\infty}\sum_{k=1}^{n_{p}}\Pr\{K=k|N=n_{p}\}\Pr\{N =n_{p}\}q_{A}\sum_{w=0}^{k-1}{k-1\choose w}q^{w}(1-q)^{k-1-w}\] \[\times\bigg{[}\frac{n_{I}}{s}u_{C}(r|n_{p},k,n_{m},n_{I})\bigg{\{} q_{I}\underbrace{P_{A}^{(\mathrm{wco},c)}}_{(\mathrm{III})}+(1-q_{I}) \mathcal{P}_{A}^{(\mathrm{wco})}\bigg{\}} \tag{19}\] \[+\frac{s-n_{I}}{s}u_{P}(r|n_{p},k,n_{m},n_{I})\mathcal{P}_{A}^{( \mathrm{wco})}\bigg{]}\Pr\{\gamma_{A}^{(m)}\geq\beta_{m}|n_{m,t},n_{p,t},w,n_{I}\},\]  
respectively. Hereafter, we use \(T_{\bullet}(r)\overset{\mathrm{(def)}}{=}T_{\bullet}(r|n_{m},n_{m,t},n_{p,t},n _{I})\) for notational brevity. Practically, the terms \((\mathrm{I})\) (also \((\mathrm{III})\)) and \((\mathrm{II})\) are not independent when \(w\geq 1\) and a closed-form expression of (18) (also for (19)) is not tractable. In this paper, we assume that the terms are independent for brevity. If \(w=0\), \((\mathrm{I})\) becomes one, and the independent assumption is valid. Now, the term \((\mathrm{II})\) can be derived based on the result in (10):  
\[(\mathrm{II})=\frac{\exp\left(-\frac{\beta_{m}c}{\mu}\right)}{\left(1+\beta_{ m}\right)^{b}\left(b-1\right)!}\sum_{i=0}^{a-1}\sum_{j=0}^{i}\frac{(b+j-1)!}{j!(i-j )!}\frac{\beta_{m}^{i}}{(1+\beta_{m})^{j}}\left(\frac{c}{\mu}\right)^{i-j}, \tag{20}\]  
where the three interim variables \(a\), \(b\) and \(c\) are defined as:  
\[\begin{cases}a=\lfloor z\alpha m_{r}L\rfloor,\\ b=\lfloor\sum_{t=1}^{z}\left(n_{p,t}\overline{m}L+n_{m,t}\alpha\overline{m}L \right)+z\left(wa\overline{m}L+n_{I}m_{I}L\right)\rfloor,\\ c=\lfloor z\eta\rfloor.\end{cases} \tag{21}\]  
From (18) and (19), the throughput of the preamble and the message transmission can be derived as:  
\[S_{T} = \sum_{r=0}^{r_{max}}G_{r}T_{\bullet}(r)=\sum_{r=0}^{r_{max}}\frac {G\prod_{i=0}^{r_{max}-1}\{1-u_{\bullet}(i)q_{A}\}T_{\bullet}(r)}{1+\sum_{j=1}^ {r_{max}}\prod_{i=0}^{j-1}\{1-u_{\bullet}(i)q_{A}\}}. \tag{22}\]
# Compact Trap-Assisted-Tunneling Model for Line Tunneling Field-Effect-Transistor Devices
## 1 Introduction  
With conventional complementary metal-oxide-semiconductor (CMOS) technology coming to the end of its life cycle owing to scaling limitations, there has been significant interest in the research and development of alternate technologies including tunneling field-effect-transistors (TFET) [1; 2; 3]. TFETs work on the principle of band-to-band-tunneling, and provide a significantly better subthreshold slope (SS) as compared to metal-oxide-semiconductor field-effect-transistor (MOSFET) devices, and its ON-current (\(I_{\text{ON}}\)) issue [4] can be augmented with the help of alternate designs, including the line tunneling type TFET or III-V material TFET. In theory, this makes the TFET an ideal candidate to replace the aging MOSFET.  
However, there is one issue that continues to be the bottleneck in the practical realization of the TFET. TFETs are very sensitive to subthreshold degradation caused by trap states [5]. Trap states cause trap-assisted-tunneling (TAT); TAT is a phonon-assisted band-to-band tunneling current generation process aided by trap states and is known to cause significant subthreshold slope degradation in TFETs. To date, there is very limited work available in the literature on the modeling of TAT mechanisms. [PERSON] et al. [6] developed a compact TAT model based on [PERSON]'s TAT model [7]. However, [PERSON]'s TAT model can be considered a first-order model; it lacks fundamental and microscopic physicalparameters that play a critical role in influencing TAT current including the phonon energy and Huang Rhys factor [8], which is a measure of the electron-phonon coupling and the lattice relaxation energy [9]. This makes fitting any experimental data with the help of the [PERSON] model very difficult and without any physical relevance. Furthermore, the Hurkx model is a piece-wise model with different equations for different trap energy levels (\(E_{\text{T}}\)) [10]. This makes its compact implementation impractical. The Schenk model [11] on the other hand, offers a more microscopic approach to TAT modeling and is easy to implement numerically. The Schenk model has been used in studies investigating the role of TAT in TFET devices. However, there is no numerical or compact implementation of the Schenk model in the literature. This paper presents a simple Schenk-TAT compact model that is applied to fit the experimental drain-source current (\(I_{\text{ds}}\))-gate-source voltage (\(V_{\text{gs}}\)) data of a line tunneling type and L-shaped TFET (LTFET) device [12]. The model is discussed in Section 2, and the results are discussed in Section 3. The conclusion is presented in Section 4.
## 2 Materials and Methods
### Preliminaries
#### 2.2.4 Shamir Threshold Secret-Sharing Scheme  
The Shamir threshold secret-sharing scheme [39], also known as Shamir secret sharing, is a threshold secret-sharing scheme based on the Lagrange interpolation formula.  
There are two important parameters in the threshold secret-sharing scheme: \(t\) and \(n\). In this scheme, \(n\) represents the number of parties involved in splitting the secret, and \(t\) is the minimum number of participants needed to recover the secret. The basic principle of the threshold secret-sharing scheme is as follows: a secret \(s\) is divided into \(n\) sub-secrets, and each party holds one sub-secret. When the secret \(s\) needs to be restored, at least \(t\) parties need to retrieve the sub-secret and restore the secret \(s\).  
The Shamir threshold secret-sharing scheme is as follows:  
(i) Choose a large prime \(p\), and assume that \(s(s\in Z_{p}^{*})\) is the secret that requires at least \(t\) parties to recover.  
(ii) Construct a random-degree \(t-1\) polynomial \(f(x)=a_{0}+a_{1}\cdot x+\cdots+a_{t-1}\cdot x^{t-1}\), where \(a_{0}=s\) and \(a_{1},\ldots,a_{t-1}\in Z_{p}^{*}\).  
(iii) Each participant \(i\), \(i\in\{1,\ldots,n\}\), holds a sub-secret \((i,f(i))\), which can be viewed as a point \((x_{i},y_{i})\) in the Lagrangian interpolation formula.  
(iv) Since \(a_{0}=s\), calculating \(f(0)=a_{0}\) restores the secret. Using Lagrange interpolation, \(f(x)\) is restored with the sub-secrets of any \(t\) parties, and then, \(f(0)\) is computed as follows:  
\[f(x)=\sum_{i=1}^{t}(f(i)\cdot\prod_{j=1,j\
eq i}^{t}\frac{x-j}{i-j})\quad mod\quad p \tag{8}\]  
\[f(0)=\sum_{i=1}^{t}(f(i)\cdot\prod_{j=1,j\
eq i}^{t}\frac{j}{j-1})\quad mod\quad p \tag{9}\]
# Pattern Classification Based on RBF Networks with Self-Constructing Clustering and Hybrid Learning
###### Abstract  
Radial basis function (RBF) networks are widely adopted to solve problems in the field of pattern classification. However, in the construction phase of such networks, there are several issues encountered, such as the determination of the number of nodes in the hidden layer, the form and initialization of the basis functions, and the learning of the parameters involved in the networks. In this paper, we present a novel approach for constructing RBF networks for pattern classification problems. An iterative self-constructing clustering algorithm is used to produce a desired number of clusters from the training data. Accordingly, the number of nodes in the hidden layer is determined. Basis functions are then formed, and their centers and deviations are initialized to be the centers and deviations of the corresponding clusters. Then, the parameters of the network are refined with a hybrid learning strategy, involving hyperbolic tangent sigmoid functions, steepest descent backpropagation, and least squares method. As a result, optimized RBF networks are obtained. With this approach, the number of nodes in the hidden layer is determined and basis functions are derived automatically, and higher classification rates can be achieved. Furthermore, the approach is applicable to construct RBF networks for solving both single-label and multi-label pattern classification problems.  
pattern classification; RBF network; self-constructing clustering; basis function; steepest descent backpropagation; least squares method Article
## 4 Results and Discussion
### Feature Scoring
#### 4.5.1 Performance Evaluation  
Based on the evaluation presented in Table 6, the performance of different ML models was evaluated. The results of each implemented model are discussed as follows: The LR model exhibited a strong overall performance, with a relatively low accuracy of 0.88 and F1-score of 0.92 as compared to RF and XGB. It maintained an excellent balance between precision (0.92) and recall (0.92), with a respectable AUC of 0.93 and good specificity at 0.80. SVM showed decent accuracy at 0.72 but fell short in terms of its F1-score of 0.67, indicating potential challenges in balancing precision (0.98) and recall (0.51). The AUC was reasonable at 0.88, while specificity was notably high at 0.98. NB performed reasonably well, with an accuracy of 0.84 and an F1-score of 0.83. It exhibited good precision of 0.96 but a slightly lower recall of 0.73, with the AUC standing at 0.84; specificity was solid at 0.96. RF outperformed the other implemented models with the highest accuracy of 0.96 and demonstrated impressive results across other metrics, achieving a high F1-score of 0.96, indicating a well-balanced trade-off between precision (0.99) and recall (0.93). Additionally, it boasted a substantial AUC (0.98) and maintained a noteworthy specificity of 0.96. XGB also demonstrated excellent performance in terms of classification accuracy (0.93), with an F1-score of 0.96. This indicated its ability to achieve a balance between precision and recall of 0.96. Additionally, XGB showcased strong discriminatory power, as evidenced by its AUC of 0.97 and specificity of 0.95. KNN exhibited a decent accuracy of 0.92 and an F1-score of 0.91. It provided a reasonable balance between a precision of 0.97 and a recall of 0.87. The AUC was 0.93, and the specificity was 0.89.
## 2 Materials and Methods
### Uncertainty Analysis of NH\({}_{3}\) Emission Factor by Monte Carlo Simulation  
In South Korea, the uncertainty of atmospheric pollutants is determined based on the data attribute rating system (DARS) methodology of the Environmental Protection Agency (EPA), which was grounded on expert judgment [15; 16]. However, the European Monitoring and Evaluation Program (EMEP)/European Environment Agency (EEA) states that the quantitative uncertainty assessment on emissions factors must be proceeded based on IPCC's 2006 methodology used in greenhouse gases [17]. To evaluate the level of uncertainty in the NH\({}_{3}\) emission factor calculated for biogas production facilities, this study used the Monte Carlo simulation recommended by the Intergovernmental Panel on Climate Change (IPCC) 2006 guideline [18].  
The Monte Carlo simulation generates random numbers based on the input variables and designates the probability density function (PDF) to assess the uncertainty, and it is commonly used in the environmental sector to assess the uncertainty [19; 20; 21]. The Monte Carlo simulation analysis involves four stages, and the procedure is outlined in Figure 2. The first stage involved selecting the model, which was accomplished by creating the NH\({}_{3}\) emission factor determination work sheet for the biogas production facility. The second stage involved the verification of the PDF of the input variables necessary for developing the emission factor. By verifying the suitability of the data distribution of each input variable, the PDF was selected, and the significance level of 5% was selected for the hypothesis test. The PDF was determined based on the verification results of each input variable, namely NH\({}_{3}\) emissions concentration, exhaust gas flowrate, biogas production, and amount of waste processing required for the development of NH\({}_{3}\) emission factor. In the third stage, the Monte Carlo simulation was executed, where the \"Crystal ball\" program was used to proceed with random sampling. Lastly, the simulated results were divided into 95% confidence intervals to estimate the uncertainty.
## 1 Introduction
To address the performance issues faced by AOS multiplexing under space network traffic, this paper proposes an efficient network AOS comprehensive multiplexing algorithm based on elastic time slots. First, the space network traffic is categorized into synchronous traffic, asynchronous real-time traffic, and asynchronous non-real-time traffic according to its characteristics. A comprehensive multiplexing model with strong scalability is established based on the packet multiplexing layer, virtual channel multiplexing layer, and decision layer. Second, independent multiplexing algorithms are designed for each traffic type to accommodate their specific traffic characteristics, thereby meeting the quality-of-service requirements for different types of traffic. Special attention is given to asynchronous real-time services, which exhibit strong randomness and have high latency and efficiency demands. The traffic prediction algorithm in Reference [26] is adopted, in the packet multiplexing layer, the multiplexing delay is optimized based on traffic prediction, and, in the virtual channel multiplexing layer, scheduling decisions are made by the virtual channel scheduling status, transmission frame scheduling status, virtual channel priority status, and traffic prediction status, which enables the scheduling algorithm to adjust decisions proactively before traffic arrival. Finally, an AOS comprehensive scheduling strategy based on elastic time slots is proposed. Starting with an initial allocation of time slots, the three traffic types can dynamically preempt time slots based on the current slot occupancy and service request conditions. The boundaries of service time slots are flexible, allowing the multiplexing algorithm to efficiently adapt to dynamic changes in the channel, thereby improving the overall multiplexing efficiency.
## 3 Proposed Method
### Loss Function
#### 3.5.2 Patch-Wise Contrastive Loss  
The main idea of contrastive loss is to make the positive samples closer to each other while making the negative samples further apart. For an image patch in the generated image, we call it a query sample and find the corresponding image patch in the original image; these two image patches are called positive samples, while the image patch in the generated image and other image patches in the original image are called negative samples. Specifically, we picked a query sample \(v\) and found its corresponding patch in the original image as a positive sample \(v^{+}\), and then found N negative samples \(v^{-}\) and mapped them to K-dimensional vectors, where \(v\in\mathbb{R}^{K}\), \(v^{+}\in\mathbb{R}^{K}\), and \(v^{-}\in\mathbb{R}^{N\times K}\); then, we built an \((N+1)\)-way classification problem using cross-entropy loss to compute the probability of selecting the positive sample over negative samples, with the following equation:  
\[l\big{(}v,v^{+},v^{-}\big{)}=-log\Bigg{[}\frac{exp(v\cdot v^{+}/\tau)}{exp(v \cdot v^{+}/\tau)+\sum_{n=1}^{N}exp\big{(}v\cdot v_{n}^{-}/\tau\big{)}}\Bigg{]}, \tag{8}\]  
where \(\tau\) is the temperature coefficient to adjust the distance between the query sample and other samples, and the default is 0.07. \(v_{n}^{-}\) denotes the \(n\)-th negative sample.  
We used the encoding module of the generator and a two-layer MLP to extract features from the input image, where an element in the output feature map of each layer represents an image patch in the input image, and the deeper layer represents the larger image patch. We selected feature maps from L of these network layers, inputted them into the two-layer MLP, and obtained different-scale feature samples.  
We aimed to make each image region of the input image match the corresponding image region of the generated image, which means that each image patch in the generated image should reflect the content of the corresponding image patch in the input image, as shown in Figure 7. At the same time, we also used the contrastive loss for the haze-free image to prevent the generator from making unnecessary changes.  
\[\mathcal{L}_{Contrastive}(G,MLP,X)=\mathbb{E}_{x\sim X}\sum_{d=1}^{L}\sum_{s= 1}^{S_{l}}l\left(\hat{z}_{l}^{s},z_{l}^{s},z_{l}^{S\setminus s}\right), \tag{9}\]  
\[\mathcal{L}_{Contrastive}(G,MLP,Y)=\mathbb{E}_{y\sim Y}\sum_{d=1}^{L}\sum_{s= 1}^{S_{l}}l\left(\hat{z}_{l}^{s},z_{l}^{s},z_{l}^{S\setminus s}\right), \tag{10}\]  
where \(l\) represents the selected L-th layer, \(S_{l}\) represents one of the image patches in the L-th layer, \(\hat{z}_{l}^{s}\) represents the query sample, and \(z_{l}^{s}\) and \(z_{l}^{S\setminus s}\) represent the positive samples and negative samples, respectively.
# Biocompatibility of Membranes Based on a Mixture of Chitosan and _Lythri herba_ Aqueous Extract
## 4 Discussion
The presence of a prolonged effect of thrombin synthesis in membranes with a higher Ly concentration can be correlated with the level of bioactive compounds of the extract included in CS-Ly bonds. However, the prothrombin effect requires additional evaluations to identify the action mechanisms of the extract on the coagulation cascade. A series of studies revealed the importance of knowing thrombin activity both in pathologies and in the use of plant extracts [94]. Chitosan is a biopolymer known for its hemostatic properties, which can be attributed to its ability to create artificial clots when it comes into contact with blood. Chitosan induces platelet adhesion, aggregation and activates intrinsic blood coagulation [95]. The hemostatic mechanism of chitosan involves the agglutination of erythrocytes of the classical coagulation system; in other words, the positively charged structure of chitosan attracts the negatively charged red cells, leading to the appearance of the phenomenon of agglutination and, therefore, coagulation [96]. The ability of bacteria to develop biofilm on various surfaces is a crucial characteristic associated with pathogenicity, particularly in the context of biomaterial manufacturing technologies. The Ly extract composition comprises compounds that engage in interactions with the chitosan polymer, resulting in alterations to the surface of the membranes in conjunction with variations in chitosan concentration. In contrast to findings from previous studies, the results indicate a reduced level of bacterial adhesion on the CS-Ly membranes examined, in comparison with other materials commonly employed in the production of dental implants, after a 24 h period [97]. These topographic changes of the membrane surfaces also change the contact with the bacteria and make the adhesion different [98]. The results obtained are encouraging and suggest that adjusting the concentrations of the two components in the mixture may serve as a modulating factor for the effects of bacterial anti-adhesiveness and biocompatibility, as well as correlation with the studies about standardization of the extract [99]. Nevertheless, the subsequent studies endeavor to consolidate the information pertaining to prolonging the duration of exposure beyond 24 h while also diversifying the control experiments.
###### Abstract  
The paper presents a report on the performance evaluation of a newly developed smart logistics system (SLS). Field tests were conducted in Spain, Germany, and Sweden. The evaluation focused on the performance of a smart box tool (used to capture information during biomass transport) and a web-based information platform (used to monitor the flow of agricultural pruning from farms to end users and associated information flow). The tests were performed following a product usability testing approach, considering both qualitative and quantitative parameters. The detailed performance evaluation included the following: systematic analysis of 41 recordable parameters (stored in a spreadsheet database), analysis of feedback and problems encountered during the tests, and overall quality analysis applying the product quality model adapted from ISO/IEC FDIS 9126-1 standard. The data recording and storage and the capability to support product traceability and supply chain management were found to be very satisfactory, while assembly of smart box components (mainly the associated cables), data transferring intervals, and manageability could be improved. From the data retrieved during test activities, in more than 95% of the parameters within 41 columns, the expected values were displayed correctly. Some errors were observed, which might have been caused mainly by barriers that could hinder proper data recording and transfer from the smart box to the central database. These problems can be counteracted and the performance of the SLS can be improved so that it can be upgraded to be a marketable tool that can promote sustainable biomass-to-energy value chains.  
smart logistics system; information platform; pruning biomass; performance evaluation; product quality model; product usability testing
## 3 Local Differential Privacy
### Poisoning Attack  
A poisoning attacks in LDP [5; 6; 27] involves an attacker injecting fake users into the system and manipulating the analytics results of the server via sending carefully crafted data from the fake users to the server.  
Figure 1 shows the framework of a poisoning attack. We assume that attackers can inject \(m\) fake users into the system. They have access to public information regarding the targeted LDP scheme, such as the privacy budget \(e\) and perturbation procedures. Within a set of \(n\) genuine users, the server estimates the frequency and mean of \(r\) targeted keys among \(n+m\) users. The attacker intentionally manipulates the estimated frequency and mean value of a set of targeted keys. We assume that the attacker targets \(r\) keys out of \(d\), aiming for manipulation of the frequencies and mean values.  
[PERSON] et al. [6] proposed three types of poisoning attacks:  
1. Maximum gain attack (M2 GA): All fake users generate optimal fake outputs for perturbation messages to maximize the gains of the frequency and mean. That is, they select a targeted key \(k\) (selected randomly from \(r\) targeted keys) and send \(\langle 1,1\rangle\) to the server.
2. Random message attack (RMA): Each fake user uniformly selects a message from the domain and sends \(\langle 0,0\rangle\), \(\langle 1,-1\rangle\), and \(\langle 1,1\rangle\) with probabilities \(1/2\), \(1/4\), and \(1/4\), respectively.  
3. Random key-value pair attack (RKVA): Each fake user randomly selects a key \(k\) from the target key set with a specified value 1 and perturbs \(\langle 1,1\rangle\) according to the protocol.