esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 2 Background
### Hidden Markov Models  
An HMM is a stochastic statistical model of a discrete Markov chain of a finite number of hidden variables \(X\) that can be observed by a sequence of a set of output variables \(Y\) from a sensor or other sources. The probability of transitioning from one state to another in this Markov chain is time-invariant, which makes the model stationary. The observed variables \(Y\) are stochastic with the observation (emission) probabilities, which are also time invariant. The overall HMM consists of \(n\) distinct hidden states of the Markov chain and \(m\) corresponding observable symbols. In general, the observations can be discrete or continuous, however, in this work, we focus on the discrete observations. The variable \(X\) has \(n\geq 2\) hidden states, denoted by \(x_{i},1\leq i\leq n\), and the variable \(Y\) has \(m\geq 2\) observable symbols, denoted by \(y_{j},1\leq j\leq m\). Formally, an HMM \(\Lambda\) is specified by a set of parameters \((A,O,\Gamma)\) that are defined as follows:  
1. The prior probability distribution (initial vector) \(\Gamma\) has entries \(\gamma_{i}=p(x_{i}),1\leq i\leq n\), which are the probabilities of state \(x_{i}\) being the first state in the Markov chain.
2. The transition matrix \(A\) has entries \(a_{i,j}=p(x_{j}|x_{i}),1\leq i,j\leq n\), which are the probabilities of transit from state \(x_{i}\) to state \(x_{j}\) in the Markov chain.
3. The observation matrix \(O\) has entries \(o_{i,j}=p(y_{j}|x_{i}),1\leq i\leq n,1\leq j\leq m\), which are the probabilities to observe \(y_{j}\) if the current state is \(x_{i}\).  
An example of an HMM where \(X\) has two states and \(Y\) three symbols is shown in Figure 1a. The two states are \(x_{1}\) and \(x_{2}\) and based on them three symbols \(y_{1},y_{2}\) or \(y_{3}\) are observed. The parameters of the HMM (including initial vector, transition matrix and observation matrix) are also shown in Figure 1a.  
The dynamic Bayesian network representation of the HMM in Figure 1a is shown in Figure 1b, unrolled for \(T\) time slices [8; 9]. The initial vector, transition matrix and observation matrix of the HMM are represented by the labels \(\Gamma\), \(A\) and \(O\) respectively, attached to the nodes in the Bayesian network graph. The superscript for the variables \(X\) and \(Y\) and their values indicate the time slice under consideration.  
In this paper, \(\mathbf{y_{e}^{t}}\) denotes the actual evidence for the variable \(Y\) in time slice \(t\) and \(\mathbf{y_{e}^{t_{i}t_{j}}}\) represents a sequence of observations \(\mathbf{y_{e}^{t_{i}}},...,\mathbf{y_{e}^{t_{j}}}\) where \(\mathbf{e}\) is a sequence of observations from the set \(y_{j},1\leq j\leq m\). The representation \(x_{i}^{t}\) denotes the actual state \(x_{i},1\leq i\leq n\), for the variable \(X\) in time slice \(t\).
# Camouflaged Object Detection That Does Not Require Additional Priors
## 1 Introduction
Overall, our work makes the following main contributions:  
* For the camouflaged object detection (COD) problem, we introduce a novel Camouflage Cues Guidance Network, i.e., CCGNet. This network incorporates an Adaptive Feature Fusion Module (AFFM) to enrich the model's comprehension of the overall structure of camouflaged objects by effectively extracting and integrating the inherent semantic information present within the image itself. * We introduce two crucial modules, namely, the foreground separation module (FSM) and the edge refinement module (ERM). These modules utilize the camouflage cues  
Figure 1: Visual examples of camouflaged object detection in challenging scenarios showcase our algorithm’s capabilities. When compared to recent COD algorithms (e.g., BGNet, and S-MGL) that rely on boundary priors, our algorithm achieves superior boundary detection results without the need for any prior information and effectively mitigates uneven pixel distribution. generated by the AFFM to thoroughly investigate the relevant semantic details within the image and improve the edge representation of camouflaged objects. * Extensive experiments conducted on three traditional benchmark datasets show that our model outperforms state-of-the-art models in all four metrics.
## 3 Results

Table 1: Two-way analysis of variance (ANOVA) comparing the variables between the different methods and overloads. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Overload**} & \multicolumn{2}{c}{**FP vs. HRD**} \\ \cline{3-4}  & & **MD (95\% CI)** & **r (95\% CI)** \\ \hline F\({}_{\rm r}\) (N) & MiL & 3.33 (2.04–4.61) *** & 0.10 (\(-\)0.28–0.46) \\  & MoL & 2.44 (0.76–4.12) ** & 0.32 (\(-\)0.06–0.62) \\  & HiL & 4.14 (1.86–6.43) *** & 0.48 (0.12–0.72) \\ \hline F\({}_{\rm avg}\) (N/kg) & MiL & 0.05 (0.03–0.06) *** & 0.99 (0.98–0.99) \\  & MoL & 0.04 (0.01–0.06) ** & 0.99 (0.97–0.99) \\  & HiL & 0.05 (0.02–0.08) ** & 0.99 (0.97–0.99) \\ \hline P\({}_{\rm avg}\) (W/kg) & MiL & 0.22 (0.11–0.32) ** & 0.99 (0.97–0.99) \\  & MoL & 0.18 (0.05–0.30) ** & 0.98 (0.96–0.99) \\  & HiL & 0.25 (0.11–0.39) ** & 0.98 (0.96–0.99) \\ \hline F\({}_{\rm 0}\) (N/kg) & MiL & 0.10 (0.08–0.13) *** & 1.00 (0.99–1.00) \\  & MoL & 0.46 (0.36–550) *** & 0.98 (0.96–0.99) \\  & HiL & 0.42 (0.32–0.52) *** & 0.98 (0.95–0.99) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean difference and correlation with 95% confidence intervals in variables between the force plate (FP) and the hydraulic resistance device (HRD). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Overload**} & \multicolumn{2}{c}{**FP vs. HRD**} \\ \cline{3-4}  & & **MD (95\% CI)** & **r (95\% CI)** \\ \hline \(\mathrm{v_{0}}\) (m/s) & MiL & 0.02 (\(-\)0.00–0.04) & 1.00 (0.99–1.00) \\  & MoL & \(-\)0.07 (\(-\)0.10–0.05) *** & 0.99 (0.97–0.99) \\  & HiL & \(-\)0.07 (\(-\)0.12–0.03) *** & 0.99 (0.99–1.00) \\ \hline \(\mathrm{P_{max}}\) (W/kg) & MiL & 0.24 (0.18–0.31) *** & 1.00 (0.99–1.00) \\  & MoL & 0.70 (0.51–0.89) *** & 1.00 (0.99–1.00) \\  & HiL & 0.49 (0.35–0.62) *** & 0.99 (0.98–1.00) \\ \hline \end{tabular} F\({}_{0}\), maximal theoretical horizontal force; E, FP, force plate; F\({}_{\mathrm{avg}}\), average force; F\({}_{\mathrm{r}}\), resistance force; HiL, high overload; HRD, hydraulic resistance device; MD, mean difference; MiL, minimal overload; MoL, moderate overload; N, Newtons; P\({}_{\mathrm{avg}}\), average power; P\({}_{\mathrm{max}}\), maximal theoretical power; \(\tau\), Pearson’s product–moment correlation coefficient; \(\mathrm{v_{0}}\), maximal theoretical velocity; W, Watts; and 95% CI, 95% confidence interval. ***\(p<0.001\) and ***\(p<0.01\).  
\end{table} 
Table 2: _Cont._
## 4 Mission Reliability Modeling, Allocation, and Analysis Method Design
### Task Reliability Modeling Design  
The dynamic reconfiguration behavior of the ship electronic information system is manifested in the fact that when a component of the system fails, it can be replaced by a new component through the dynamic reconfiguration technology to improve the reliability and performance of the system. At the same time, dynamic reconfiguration technology can also dynamically optimize and adjust the system according to the mission requirements and resources, thus improving the flexibility and adaptability of the system so it can better cope with different mission failures and resource failures. AADL can be used to model the hardware, software, system architecture, and functional interfaces of a highly complex real-time embedded system, which is consistent with the loosely-coupled hardware and software, and dynamically reconfigurable business characteristics of marine electronic information systems. Therefore, this study adopts AADL to model the reliability of reconfigurable ship electronic information systems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Fault Factors** & **Fault Mode** \\ \hline Common & Environmental stress & The resources and hardware that are mutually \\ cause failure & changes & backed up cannot function properly \\  & Memory damage & Inaccurate output of service software using the \\  & & same memory block \\ \hline Cascading failure & Network device failure & Network overload \\  & CPU damage & CPU resource depletion \\ \hline \hline \end{tabular}
\end{table}
Table 4: Library of common failure modes for reconfigurable ship electronic information systems.  
Figure 5: Longitudinal transmission model of faults in reconfigurable ship electronic information systems.  
Specifically, in this study, the OSATE2 software (version 2.10) developed by Carnegie Mellon University was selected for AADL reliability modeling. OSATE2 is an Eclipse-based open-source tool to support AADL modeling, analysis, and verification [47]. OSATE2 is developed based on the Eclipse platform. Thus, it has good extensibility and can be extended with various functions through a plug-in mechanism
## 3 Results and Discussion
### Performance of the FTSW Model on Quinta de Pancas Dataset  
As was mentioned, the ETo for Quinta de Pancas was calculated using Equation (10). First, Equation (10) was evaluated using the Colinas do Douro dataset, and the validation yielded an RMSE of 1.10 mm and an \(R^{2}\)-score of 0.92%. The evaluation was performed to ensure that the formula can estimate the ETo with reasonable error.  
Figure 10 shows the monthly accumulated rainfall and estimated ETo using Equation (10) for Quinta de Pancas from 2002 to 2004. Note that the recording of the climate data for this dataset was not continuous. The value of zero for the ETo in the plot shows these missing data. A comparison of Figures 3 and 10 shows that the maximum monthly ETo totals at Colinas do Douro were in the range of 175-200 for each season and between 140 and 155 for Quinta de Pancas.  
After ETo estimation, the BiLSTM model was used to predict the FTSW for Quinta de Pancas. Table 7 shows the performance of the FTSW prediction model for Quinta de Pancas. Compared to Colinas do Douro, the \(R^{2}\)-score decreased by 6% and the RMSE increased by 1.39%. Part of this decrease may be due to the error in the estimation of the ETo (see Table 7), but in general, the model was able to predict the FTSW at Quinta de Pancas with a reasonable prediction performance.  
Figure 11 compares the observed values of the FTSW with the value predicted by the BiLSTM model. The points in the scatter plot must move to the yellow line to obtain a good approximation. The fact that the points lie along the line shows that the model represented the relationships very well, as can be observed.  
\begin{table}
\begin{tabular}{l l l} \hline
**Location** & **RMSE** & \(R^{2}\)**-Score** \\ \hline Quinta de pancas & 10.22 & 0.87 \\ \hline Colinas do Douro & 8.43 & 0.93 \\ \hline \end{tabular}
\end{table}
Table 7: Performance of the trained BiLSTM model to predict the FTSW one day ahead on the Quinta de Pancas test dataset.  
Figure 10: Monthly sum of rainfall and estimated ETo for Quinta de Pancas from 2002 to 2004.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 6 Performance Evaluation
### Communication Overhead Comparison  
Additionally, to compare the proposed protocol with the existing authentication schemes according to communication overhead, we need to assume the parameter sizes. Since \(p^{\prime}\) and \(p\) are prime numbers of 64 bytes (512 bits) and 20 bytes (160 bits), respectively, the length of the elements in \(G_{1}\) and \(G_{2}\) are 128 bytes and 40 bytes separately. We assume the length of a one-way hash function's output is 20 bytes, the length of a timestamp is 4 bytes, the length of an identity is 20 bytes, and the length of the symmetric key encryption or decryption (AES-512) function's output is 64 bytes. Finally, we present the communication costs of our scheme and other schemes in Table 4. In the proposed scheme, Figure 5 shows that the communication messages between one RUS and a vehicle in the authentication phase are \(M_{2}:\{E_{K_{o}}(K_{u}),t_{2},\phi_{2}=H(E_{K_{u}}||H(R_{v})||t_{2})\}\) and \(H(rn+1)\). Hence, the total communication costs of our scheme are \((64+4+20)+20=108\) bytes and \(108n\) bytes for \(n\) RUSs. The cost of communication of the others can be calculated in the same way. And the results in Table 4 and Figure 7 show the proposed scheme communication overhead is less than others.  
Figure 6: The computation overhead in authentication. Chatterjee et al. proposed the scheme Chatterjee et al. (2019); Aman et al. proposed the scheme Chatterjee et al. (2019), Cui et al. proposed the scheme Chatterjee et al. (2019), and Kumar et al. proposed the scheme Kumar et al. (2020).  
Figure 7: The communication overhead in authentication. Chatterjee et al. proposed the scheme Chatterjee et al. (2019), Aman et al. proposed the scheme Chatterjee et al. (2019), Cui et al. proposed the scheme Chatterjee et al. (2019), and Kumar et al. proposed the scheme Kumar et al. (2020).
# Addressing Sustainability within Public Procurement of Food: A Systematic Literature Review
## 1 Introduction  
Activities related to the production and consumption of food are among the most significant contributors to climate change, accounting for 20-30% of anthropogenic greenhouse gas (GHG) emissions [1; 2; 3]. These impacts occur throughout the entire life cycle of food, from production to consumption and the handling of food waste [3]. With an increasing population, the environmental impacts of food production and consumption will also continue to grow, despite efficiency gains [4].  
While the pressing need for a sustainable food system is widely acknowledged, there is currently no consensus on how to transform it. Many articles outline different policy interventions to influence the sustainability of the food system, aimed at improving technologies, economic viability, and behavioral changes [5; 6; 7]. Public procurement can be defined as the procurement of goods, services or constructions on behalf of a public authority to meet certain criteria or requirements [8]. Public procurement has been highlighted in a number of studies as an effective approach to motivate more sustainable production methods. As such, the increased demand for sustainable products and services through public purchasing is thought to orient production and consumption in a more sustainabledirection [9; 10; 11; 12]. As public procurement can amount to roughly 20-30% of the gross domestic product [11; 13; 14; 15], there is a significant potential to influence more sustainable production and consumption of food [16].  
Different approaches for considering sustainability in public procurement have been developed, such as Green Public Procurement (GPP) and Sustainable Public Procurement (SPP). While GPP mainly focuses on environmental impacts, SPP aims to include environmental, social, health, and socio-economic aspects in the procurement [17]. Few articles have identified how sustainability is addressed in public procurement of food, despite previous literature reviews of food-related public procurement [18; 19; 20]. Salient information on how and what sustainability aspects are addressed and motivated with respect to food is essential to develop public procurement and build consensus [21; 22; 23; 16; 24].  
This article aims to review and analyze how sustainability is framed in the scientific literature on public procurement of food--i.e., what aspects of sustainability are emphasized and what practices are identified as sustainable. Through a systematic literature review, this study investigates what aspects are the main focuses when addressing the three pillars of sustainability (environmental, social, and economic), and how these are discussed and motivated in the literature.
## 3 Meteorology
### Digital Image Correlation in SCB Test  
Digital image correlation (DIC) technology was adopted to record the surface displacement and strain distribution of SCB specimens [21]. The experimental setups are shown in Figure 4. An industrial digital camera (resolution 1626 \(\times\) 1236, focal length 25 mm, pixel size 4.4 \(\upmu\)m, 14 fps @max resolution) was employed to capture frames during the process of fatigue loading. The camera was positioned perpendicular to a specimen and at a constant distance away from it. A lighting system, composed of two white lights, provided adequate illumination of the specimen inside the chamber. The camera was set to acquire five frames per second at the beginning of the test.  
Figure 3: ECC semi-circular bending (SCB) specimen preparation process.
## 4 Implementation and Discussion
### Performance Analysis
#### 4.2.10 Throughput Performance  
As this is an ongoing work that is developing its own blockchain implementation platform, this study provides only an overview of system performance. In bitcoin and Ethereum, the block size is limited, and block time is higher. For example, in bitcoin block size is 1 MB and the average block time is 10 min. That is, including network constraints and limitations, average throughput of bitcoin is between 3.3 to 7 or 1,000,000/10/60/230 = 7.25 tx/second (where a small transaction is assumed to 230 bytes) [41; 42]. On the contrary, multichain does not restrict block size and time. Therefore, we can even set block size up 1 GB, and block time to 2 s. That is, including network constraints and limitations, average throughput of used multichain blockchain can be up to 2 million tx/second [79]. Therefore, block size and transmission latency are the major factors to decide overall throughput. Firstly, we are updating our decision judging factors to finalize our block size. Secondly, we are planning to test the PoF based scheme in a large environment (in the Inje University Hospital) [80]. So this study plans to include exact network performances (block size, block time) in the future. In addition, it plans to compare network performance of other studies as well.
## 4 Methodology
### Domain Clustering Based on Multi-Path Transmission  
In satellite network resource allocation and task scheduling optimization, traditional methods often overlook the dynamic nature of the network topology and the complex interactions between nodes. Existing optimization methods mainly focus on static models or single-path resource allocation, lacking in-depth consideration of dynamic changes in network topology, node-to-node game behaviors, and multi-path transmission strategies. Therefore, this paper proposes an optimized method based on Network Coding (NC) and Multi-Path Transmission Strategies, and incorporates game theory and cluster head election techniques for improved performance, addressing the inadequacies of traditional methods in dynamic environments.  
The Figure 2 illustrates the SDN-based domain clustering and collaborative scheduling framework for large-scale satellite networks. The global SDN controller gathers real-time state information from satellite nodes, performs dynamic domain partitioning, and optimizes multi-path scheduling. By integrating Nash bargaining game models and resource reallocation mechanisms, the framework ensures efficient resource utilization, fair task allocation, and rapid failure recovery. Tasks originating from ground users are distributed across multiple satellite logical domains, enhancing load balancing and system robustness in highly dynamic network environments.
## 7 Conclusions  
To improve the reliability of communication transmission in the presence of malicious jamming, this paper proposes a multi-parameter control anti-jamming algorithm for wireless communication systems based on LQR. Simulation results demonstrate that this algorithm enables the system to quickly restore the BER to the target value under unknown jamming conditions, showing more stable BER and faster power response speed, without requiring an awareness of the jamming patterns and behaviors. Future research could explore more complex models and algorithms by integrating machine learning and deep learning techniques to enhance system performance and adaptability.  
Conceptualization, H.Y., Y.N., K.Z. and K.Y.; methodology, H.Y., Y.N., K.Z. and R.G.; software, H.Y. and R.G.; validation, H.Y., Y.N. and K.Z.; formal analysis, H.Y., R.G. and K.Y.; investigation, H.Y., R.G. and K.Y.; resources, H.Y.; data curation, H.Y.; writing--original draft preparation, H.Y.; writing--review and editing, H.Y., Y.N. and K.Z.; visualization, H.Y.; supervision, H.Y.; project administration, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are not available due to privacy.  
The authors declare no conflicts of interest.  
Figure 9: Comparison of different algorithms under random pulse jamming for systems with and without the implemented switching mechanism.
## 1 Introduction
However, this method depends on a precise ionospheric delay model (precise enough to fix the WL ambiguity with a wavelength of \(\sim\)86 cm), which devalues its application. [PERSON] and [PERSON] [28] adopted this idea and found that 92.4% of all fractional parts of GLONASS WL ambiguities agreed well within \(\pm\)0.15 cycles, but the efficiency of PPP-IAR for GLONASS is lower than that for the GPS; only 63% of hourly solutions were fixed for GLONASS while 97% were fixed for the GPS. [PERSON] and [PERSON] [29] used a regional network of receivers with mutual separation 300 km to calculate ionospheric corrections to aid this method. Results showed that the GPS + GLONASS fixing percentage with observation length 10 min could be improved to 87.50%. [PERSON] et al. [30] calibrated the odd cycle in the HMW combination to achieve GLONASS PPP-IAR, showing that the positioning accuracy of GPS + GLONASS PPP could be improved substantially after ambiguity fixing. [PERSON] et al. [31] compared WL phase-combination-based GLONASS PPP-IAR with HMW-combination-based GLONASS PPP-IAR, proposing that the HMW-based method is independent of the ionosphere model and therefore far superior to the latter over large areas, where one cannot obtain a precise ionosphere model. According to the reviews in the previous paragraphs, it is still a challenge to resolve the GLONASS PPP ambiguity with heterogeneous receivers. In the present study, we propose a method for calibrating GLONASS P1/P2 IFCBs to achieve PPP-IAR using the HMW method over heterogeneous rover receivers. The proposed method is validated first with the same dataset of homogeneous receivers used by [PERSON] et al. [31] as a reference, to check its efficiency. We then use a network of mixed receivers in France to examine their efficiency over heterogeneous receivers. The remainder of the paper is organized as follows. We first describe the method for UPD estimation at the server. We then introduce the proposed method for GLONASS IFCB estimation in detail. We use a network of homogeneous receivers and a network of heterogeneous receivers to validate the method. Finally, discussion and concluding remarks are presented based on our findings.