esa-sceva/satcom-chunk-collection huggingface dataset:
# A Model of Information Visualization Interpretation
## 2 Materials and Methods
### General Case
#### 2.4.2 Side Lengths  
Recalling Equation (4), in order to find the probability distribution of \(D_{12}\), the distributions of \(\eta\) and \(\zeta\) must be calculated. First,  
\[\text{E}(\eta)=\text{E}(d_{2x}-d_{1x})=0.\]  
Similarly,  
\[\text{E}(\zeta)=\text{E}(d_{2y}-d_{1y})=0.\]  
For the variance,  
\[\text{Var}(\eta) =\text{Var}(d_{2x}-d_{1x})\] \[=\text{Var}(d_{2x})+\text{Var}(d_{1x})-2\,\text{Cov}(d_{2x},d_{1x})\] \[=\text{Var}(d_{2x})+\text{Var}(d_{1x})-2\,\text{corr}(d_{2x},d_{1 x})\sqrt{\text{Var}(d_{2x})\,\text{Var}(d_{1x})}\] \[=\sigma_{2x}^{2}+\sigma_{1x}^{2}-2\,\text{corr}(d_{2x},d_{1x}) \sigma_{2x}\sigma_{1x}.\]  
The term \(\text{corr}(d_{2x},d_{1x})\) requires discussion. The first correlation term encountered, \(\rho_{i}\), involves the horizontal and vertical components of the fixation about the same fixed point. This term is the correlation between the horizontal components of adjacent fixed points. On the one hand, it could be argued that the fixations in the AOIs about different fixed points are independent, which was considered in [26]. In this case, \(\text{corr}(d_{2x},d_{1x})=0\). On the other hand, considering the scanpath of the eye about the triangle, the model views the eye moving rapidly from one AOI to another. It seems possible that the horizontal offsets between the fixed points would influence each other, leading to a nonzero correlation. In the spirit of generality, then the correlation is maintained in the model, at least in this case, so that  
\[\rho_{12x}=\text{corr}(d_{2x},d_{1x}) \tag{11}\]\[\text{Var}(\eta)=\sigma_{2x}^{2}+\sigma_{1x}^{2}-2\rho_{12x}\sigma_{2x}\sigma_{1x}. \tag{12}\]  
Denoting  
\[\rho_{12y}=\text{corr}(d_{2y},d_{1y}) \tag{13}\]  
similar calculations show  
\[\text{Var}(\zeta)=\sigma_{2y}^{2}+\sigma_{1y}^{2}-2\rho_{12y}\sigma_{2y}\sigma_ {1y}. \tag{14}\]  
The covariance presents different issues. It is  
\[\text{Cov}(\eta,\zeta) =\text{E}(\eta\zeta)-\text{E}(\eta)\,\text{E}(\zeta)\] \[=\text{E}(\eta\zeta)=\text{E}((d_{2x}-d_{1x})(d_{2y}-d_{1y}))\] \[=\text{E}(d_{2x}d_{2y})-\text{E}(d_{2x}d_{1y})-\text{E}(d_{1x}d_ {2y})+\text{E}(d_{1x}d_{1y}).\]  
The first and last terms have been encountered. They are  
\[\text{E}(d_{1x}d_{1y}) =\rho_{1}\] \[\text{E}(d_{2x}d_{2y}) =\rho_{2}.\]  
The remaining terms again require modeling consideration. Again, the correlation around adjacent fixed points is encountered. This time it is the horizontal component at one point and the vertical component at the other. The previous discussion remains relevant, and the general case is considered. Here, we introduce the correlations  
\[\rho_{1y2x} =\text{corr}(d_{1y},d_{2x}) \tag{15}\] \[\rho_{1x2y} =\text{corr}(d_{1x},d_{2y}) \tag{16}\]  
allowing one to write the covariance as  
\[\text{Cov}(\eta,\zeta)=\rho_{1}\sigma_{1x}\sigma_{1y}+\rho_{2}\sigma_{2x} \sigma_{2y}-\rho_{1y2x}\sigma_{1y}\sigma_{2x}-\rho_{1x2y}\sigma_{1x}\sigma_{2y}. \tag{17}\]
## 6 Future Research Directions
### Recognition of Unseen Scenes via Knowledge Transfer  
Currently, several distinct drawbacks exist in the scene classification of HSR (high spatial resolution) RS images [34]:  
* Under no circumstance can a scene class get an appropriate classifier without labeled data using most state-of-art approaches.
* Only messages of HSR RS images can be used. The images of a scene class which are unseen cannot be recognized.
* The names of typical classes are less semantically related than those of the object classes in natural image recognition, which restricts the zero-shot (ZS) learning approaches from being employed in the RS community.  
It is normal for humans to be able to identify a new scene class. Since a huge amount of knowledge is contained and delivered to us through texts and other channels in our daily life, the former statement is reasonable. Hence, by combining seen instances and ancillary information (e.g., texts), humans may effortlessly identify new scene classes. We can use ZS learning, which is famous in natural language for transferring knowledge from seen classes to unseen classes based on accessorial information. In this paper, a novel ZS scene classification approach for High Spectral Resolution (HSR) RS images (the word2 vec model) changed the names of classes to semantic vectors for the first time [95]. Through sorting all the zero-shot models in the community of RS, only the accuracy of the ZS scene classification method was able to achieve more than 70%. This indicates that the aforementioned limitations are being dealt with.
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 2 Background
### Differences between TLS and DTLS
#### 2.4.1 Record Layer Changes  
The DTLS record layer header has two additional fields compared to TLS. An epoch field and a sequence number field called the Record Sequence Number (RSN). Endpoints use epoch numbers to determine which cipher suite protected the record fragment. The endpoints increment the epoch numbers on each _ChangeCipherSpec_ message. Epoch numbers resolve the ambiguity situation when data loss occurs during a session renegotiation or when multiple handshakes happen in close succession. TLS employs implicit sequence numbers for replay protection. The Message Authentication Code (MAC) over the TLS records also incorporates the sequence numbers. RSNs play a similar role in DTLS but are explicitly specified since records can get lost or delivered out-of-order. The DTLS record layer combines the RSN and the epoch number in a single 64-bit value while computing the MAC. DTLS increments the RSNs for each record and resets them to zero whenever the cipher state rolls over due to a session renegotiation. Implementations must therefore make sure the RSN/epoch pair is unique. DTLS can optionally perform replay detection by using the sliding window mechanism (defined in RFC 2401 [19]).  
DTLS records must fit into a single UDP datagram to prevent buffering of incomplete records on the DTLS record layer [20]. At the same size, DTLS records should not trigger IP fragmentation along the way. Loss of a single IP fragment would result in the loss of the entire datagram. In addition, Network Address Translation (NAT) devices and firewalls might drop IP fragments; IPv6 does no longer supports IP fragmentation by default. The DTLS specification [5] states that it is the responsibility of the application to perform Path Maximum Transmission Unit (PMTU) discovery, but caution is advised due to the following reasons: (1) the DTLS record framing expands the datagram size thereby lowering the effective PMTU from the application's perspective, (2) DTLS handshake message can easily exceed the MTU. TLS does not suffer from these limitations since TCP treats the TLS records as a byte stream and can thus create arbitrarily-sized segments (potentially limited by the MSS option).  
It is noteworthy that both TLS and DTLS support an extension that can explicitly limit the size of the record fragments, called Maximum Fragment Length (MFL) extension. When negotiated, MFL limits the size of the record fragments to either 512 B, 1024 B, 2048 B, or 4096 B. It is, however, not widely support by TLS servers.
## 2 Related Works
### Need for Cryptography  
The digital era has brought about significant opportunities as well as challenges. From protecting personal information and financial transactions to ensuring the confidentiality of sensitive government and corporate data, cryptography plays an essential role in preserving the integrity of digital interactions. The increasing sophistication of cyber-attacks, coupled with the growth of IoT devices and quantum computing, necessitates continuous research and innovation in cryptography.
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
## 6 The Impact of Processing on Vitamin D Levels in Foods and Mitigation Strategies
### Mitigation Strategies to Improve Vitamin D Content in Processed Foods
#### 6.2.3 Microbial Fermentation  
Among the innovative approaches to fortifying foods with Vitamin D, microbial fermentation represents a novel strategy. Certain microorganisms such as _Amycolata_ sp., _Rhodococcus erythropolis_ can convert Vitamin D3 to 1\(\alpha\),25-dihydroxy Vitamin D3 via 25-hydroxy Vitamin D3 during fermentation processes [116]. By selecting appropriate strains and optimizing fermentation conditions, manufacturers can enhance the Vitamin D content of fermented foods such as yogurt and cheese, offering consumers an additional source of this essential nutrient. Recently, EFSA evaluated the safety of Vitamin D-enriched UV-treated baker's yeast, Saccharomyces cerevisiae, as a novel food ingredient, by revealing that the UV-treated baker's yeast is capable of converting ergosterol into Vitamin D2, significantly enhancing the Vitamin D2 content of food products without compromising safety [117]. _Amycolatopsis autotrophic_ has been shown to convert Vitamin D3 to its more potent forms, 25-hydroxy Vitamin D3, and 1\(\alpha\),25-dihydroxy Vitamin D3, via microbial hydroxylation. The process was further optimized by using cyclodextrin to increase the hydroxylation efficiency significantly [118]. Since Vitamin D3 has inadequate absorption compared to its corresponding hydroxylated derivatives, an efficient biotransformation of Vitamin D3 to 25-hydroxy Vitamin D3 by a newly isolated Bacillus cereus strain was proposed by [PERSON] et al., 2020 [119]. Moreover, [PERSON] et al. reported the bioconversion of D3 derivatives for the first time by screening approximately 300 Streptomyces strains. Among them, _S. sclerotialus_ FERM BP-1370 and _S. roseosporus_ FERM BP-1574 were found to be able to introduce hydroxy group at C-1\(\alpha\) position of calcifediol and C-25 position of 1\(\alpha\)(OH)VD3, respectively. Microbial fermentation presents a viable strategy for enhancing the levels of Vitamin D3, with specific microbial species offering promising routes for the bioconversion and improved bioavailability of this essential vitamin. This approach has implications not only for dietary supplements but also for the fortification of foods, potentially contributing to better health outcomes by addressing Vitamin D3 deficiency.
# Panchromatic and Multispectral Image Fusion Combining GHS, NSST, and PCA
## 2 Methods
### Low-Frequency Fusion Rules  
The low-frequency coefficients include the primary information of the original images. [PERSON] [17] proposed an SVD of the image in the gradient domain. For image P, the steps of the gradient domain SVD are as follows:  
(1) Calculate the gradients in the row and column directions of the image P.  
(2) Form the local gradient values into an \(N\times 2\) matrix (\(G\)), with \(N\) referring to the number of local image elements.  
(3) Perform singular value decomposition of \(G\) to obtain two singular values, \(\lambda 1\) and \(\lambda 2\).  
\[G=[\
abla f1,\
abla f2\dots\
abla fN]^{T}=USV^{*} \tag{1}\]  
Here, \(\
abla fi=\left[\
abla f_{i}^{x},\
abla f_{i}^{y}\right]^{T}\) refers to the gradient values of image P in the row and column directions at image element \(i\). \(U\) is an \(N\times N\) orthogonal matrix, \(S\) is an \(N\times 2\) matrix containing the singular values \(\lambda 1\) and \(\lambda 2\) on its diagonal, and \(V^{*}\) is a \(2\times 2\) orthogonal matrix. The singular values \(\lambda 1\) and \(\lambda 2\) reflect the energy change of \(G\) in the eigenvector direction, and the magnitudes of \(\lambda 1\) and \(\lambda 2\) have different characteristics in the image  
Figure 3: Fusion flowchart of PC1 and the PAN image using the NSST transform.  
Figure 2: Flowchart of the proposed method.  
smoothing region, where the boundary is consistent with the texture direction and/or there is a more richly detailed region. Based on this, [PERSON] [16] proposed a new image structure descriptor based on \(lsd(i)=\lambda 1(i)+\lambda 2(i)\) and demonstrated its ability to reflect the basic structural information of image localization. In this paper, a low-frequency coefficient fusion rule based on SVD and a bootstrap filter is proposed, as follows.  
First, calculate the local structure descriptors \(\left(L^{PC},\ L^{PAN}\right)\) of the low-frequency component \(\left(lsd^{PC},\ lsd^{PAN}\right)\), and determine the initial weight matrices \(weight^{PC}\)and \(weight^{PAN}\) by comparing their sizes.  
\[weight^{PC}(x,y)=\left\{\begin{array}{cc}1&lsd^{PC}(x,y)\geq lsd^{PAN}(x,y) \\ 0&lsd^{PC}(x,y)<lsd^{PAN}(x,y)\end{array}\right., \tag{2}\]  
\[weight^{PAN}(x,y)=1-weight^{PC}(x,y). \tag{3}\]  
Process the weight matrix using bootstrap filtering to enhance its spatial continuity. Use \(PC1^{*}\) and \(PAN^{*}\) as bootstrap images, and apply bootstrap filtering \(weight^{PC}\) and \(weight^{PAN}\).  
\[\begin{array}{c}weight^{PC}=GF\big{(}weight^{PC},PC1^{*}\big{)}\\ weight^{PAN}=GF\big{(}weight^{PAN},PAN^{*}\big{)}.\end{array} \tag{4}\]  
The low-frequency coefficient fusion rule based on SVD and bootstrap filter can be written as:  
\[L^{F}=weight^{PC}.L^{PC}+weight^{PAN}.L^{PAN}. \tag{5}\]
## 2 Measurements and Quality Control Processes  
The radiation data on which this study is based are obtained from Athalassa meteorological station which is an inland plain location (Figure 1). The coordinates of the station are shown in Table 1.  
Figure 1: Map of Cyprus showing the location of Athalassa radiometric station.  
The climate of Athalassa is typical Mediterranean with mild winters (mean seasonal air temperature of about 10.5 \({}^{\circ}\)C) and warm and dry summers (mean seasonal air temperature of about 29.5 \({}^{\circ}\)C). The annual rainfall is about 320 mm. Rainfall is recorded mainly between October and March. The average annual sunshine duration is 3332 h. The annual average daily global radiation is 18.5 MJ m\({}^{-2}\) and the average yearly cumulative global irradiation is about 6763 MJ m\({}^{-2}\). Statistical analysis showed that clear and partially cloudy days exceed the value of 80% of the year [30].  
CG1 pyregometers obtained from Kipp and Zonen were used to measure the longwave radiation. All the sensors are factory calibrated, in accordance with the World Radiometric Reference (WRR) [33]. About 9% of the upward longwave irradiance data values are missing, while the percentage of the downward longwave irradiance which is missing is about 25%. The missing data are caused either from problematic functioning of the CG1 pyrgeometers or from problems related to the data logger system. Long missing data for both longwave irradiances are observed in the year 2017. Details about the quality control procedures for the shortwave solar radiation measurements can be found in [34].
# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
## 3 Methodology
### Model  
The proposed radar models are inspired by lidar processing and general LSTM networks from literature. The models are implemented in a modular fashion so that different architectures, e.g., for the encoder or decoder part of the network, can be replaced and combined for greater model flexibility. For additional details and analysis, we refer to the master thesis of [PERSON] [28] as the main contribution to the implementation. In the following, we recapitulate the ideas of the KPConv layer and describe our LSTM extensions, including a KPConv-based LSTM (KPLSTM) cell. The proposed models can process radar input points clouds of varying size. The KPLSTM cell associates point clouds of varying sizes over consecutive time steps. In contrast to existing LSTM cells, we incorporate the time dimension as early as in the encoding part of the network.
## 5 Simulations and Analysis
### Performance of the Proposed Attack Detection Scheme  
In this subsection, we evaluate the performance of the Siamese network used to detect FDI attacks. The batch size of the Siamese network is chosen as 20. Let \(\xi_{max}=2\) and \(\xi_{min}=1\). Training samples contain 900 historical normal sample data and 300 compromised data. As for the compromised data, we set the value templates of the exogeneous attacks from 0.01 p.u. to 5 p.u. and set the value templates of the scaling attacks from 0.01 to 5. Each sample contains 60 s of tie-line power data. In addition, 3000 observed sample data including 1500 samples of the aforementioned three types of FDI attack templates are generated as test samples.  
To illustrate the advantages of the FDI attack detection scheme studied in this paper, we choose the following five methods for comparison: (1) The proposed attack detection scheme using a cluster-based loss function (Method-A); (2) the proposed attack detection scheme using triplet loss function (Method-B) [29]; (3) the detection method using multilayer perception (Method-C) [17]; (4) the detection method using clustering-particle swarm optimization (clustering-PSO) (Method-D) [30]; and (5) the ACE forecasting method (Method-E) [15].  
True positive rate (TPR) and true negative rate (TNR) are utilized to evaluate the performance of these five methods:  
\[TPR=\frac{TP}{TP+FP} \tag{36}\]  
\[TNR=\frac{TN}{TN+FN} \tag{37}\]  
where \(TP\), \(FP\), \(TN\), and \(FN\) are the correctly detected positive samples, incorrectly detected positive samples, correctly detected negative samples, and incorrectly detected negative samples, respectively.  
Figure 6: Different data sources of tie-line power and observation error under flexible attack. (**a**) different data sources of tie-line power; (**b**) observation error.  
The simulation results considering different types of FDI attacks are shown in Figures 7 and 8.  
From Figures 7 and 8, it can be learned that the proposed attack detection scheme achieves higher \(TP\) rate and \(TN\) rate than the detection method using multilayer perception and that using clustering-PSO. In particular, when the system is compromised by flexible attacks, the proposed detection scheme has more prominent advantages than these two methods. This is because the proposed scheme is trained based on the relationship between observed data and true data. The detection method using multilayer perception and that using clustering-PSO are trained based on features of true data, which can be easily imitated by flexible attack. The lower \(TP\) rate of the ACE-forecasting method stems from the prediction errors.
## 3 Results and Discussion  
### Viable Counts of Lactic and Acetic Acid Bacteria  
Evaluating the viability of lactic acid bacteria (LAB) and acetic acid bacteria (AAB) in bicultured jiujube puree is pivotal in determining the efficacy of the matrix and its support for lactic-acetic co-fermentation. The data in Table 1 detail the proliferation of LAB and AAB strains in jiujube purees both before and after fermentation. Before fermentation, the cell concentration of all selected commercial LAB and AAB strains was approximately 8 log CFU/mL. Post-fermentation, the four strains recorded a substantial increase in viable counts. Of particular note was the significant proliferation of the _Lactobacillus heleticus_ (Lh 43) strain in puree JhAp, reaching a remarkable cell count of 12.20 log CFU/mL. Conversely, _Lacticaseibacillus casei_ (Lc 122) and _Lactiplantiobacillus plantarum_ (Lp 28) showed no statistically significant differences (\(p<0.05\)). Furthermore, the AAB cell count in JpAp was the highest, with no significant differences (\(p<0.05\)) compared to matrices JLAp and JLAp, indicating a consistent cell concentration of _Acetobacter pasteiranus_ (Ap-As.1.41) throughout the formulation. This consistency emphasizes the excellent formulation of the jiujube puree, corroborated by similar findings in Muzao juice with _L. acidophilus_ at 11.92 log CFU/mL and in Hetian juice with _L. helevticus_ and _Lp. plantarum_ at 11.86 log CFU/mL. In both cases, viable counts of four LAB strains exceeded 11 log CFU/mLafter fermentation [19]. The control group, JSON, exhibited cell counts of less than 1, suggesting effective elimination of unwanted background during pasteurization that could have interfered with the proliferation of targeted strains (_Lc. casei_ Lc 122, _L. helicticus_ Lh 43, _Lp. plantarum_ Lp 28, and _A. pasteuranus_ Ap-As.1.41) in the prepared jujube matrix. Fortunately, post-fermutation, viable counts of lactic and acetic acid bacteria in all jujube purees remained above 6.0-7.0 log CFU/mL, a level known to function as probiotics and promote health, in compliance with the recommended minimum counts of 6.0 log CFU/mL in fermented products [19]. These findings accentuate the potential of jujube puree as an excellent carrier for lactic-acetic co-fermutation studies, promising probiotic functionality and potential health benefits for consumers.
## 4 Model Design
### RNN and GRU  
Traffic control relies on precise and real-time flow prediction, and researchers have started applying deep NN methods such RNN to traffic predictions [23]. By employing RNN, which was first used for language models due to its ability to preserve long-term dependencies, researchers found that the gradients in RNN can vanish when dealing with longer time lags. To address this issue, LSTM and GRU, two types of RNN structures with forget units, were developed to enable memory cells to determine the information to discard while preserving the optimal time lags. LSTM, a model best known for its ability to remember long-term dependencies, was initially created in 1997 as a language model [24]. Although it is complex, LSTM is regarded as time-consuming to train. In 2014, a minor modification to LSTM was proposed, known as GRU, which enhances machine translation performance [25]. The Gate Recurrent Unit (GRU) is a type of recurrent neural network (RNN) that reduces matrix multiplication time without compromising performance on small datasets. Its structure is similar to that of the Long Short-Term Memory (LSTM) network; however, in GRU, the forget gate and input gate are connected to form a single update gate [26]. Figure 4 depicts a fundamental GRU unit design.  
The GRU formulas are as follows:  
\[z_{t}=\sigma\;(W_{z}\cdot[h_{t-1},x_{t}]) \tag{3}\]  
\[r_{t}=\sigma\;(W_{r}\cdot[h_{t-1},x_{t}]) \tag{4}\]  
\[\widetilde{h}_{t}=\tanh\;(W_{\widetilde{h}}\cdot[r_{t}*h_{t-1},x_{t}]) \tag{5}\]  
\[h_{t}=(1-z_{t})*h_{t-1}+z_{t}*\widetilde{h}_{t} \tag{6}\]  
where \(x_{t}\) is the input vector of training data at time \(t\), and \(h_{t}\) is the previous layer output at time \(t\). \(z_{t}\) and \(r_{t}\) are the reset and update gates, respectively. The candidate activation is represented by \(\widetilde{h}_{t}\).  
Figure 4: The internal structure of the GRU model.