esa-sceva/satcom-chunk-collection huggingface dataset:
# Extraction of Independent Structural Images for Principal Component Thermography
## 3 Extraction of Independent Components
### Proposed Approach
Thus, from (2) and (3):  
\[\sum_{m=1}^{N_{x}N_{y}}k_{tm}\overline{x}_{m}=\sum_{j=1}^{N_{p}}\alpha_{tj}P_{ j}+\widetilde{N}_{t}=\sum_{j=1}^{N_{p}}\alpha_{tj}\bigg{(}\sum_{m=1}^{N_{x}N_{y}} l_{jm}\overline{x}_{m}\bigg{)}+\widetilde{N}_{t}=\sum_{m=1}^{N_{x}N_{y}} \bigg{(}\sum_{j=1}^{N_{p}}\alpha_{tj}l_{jm}\bigg{)}\overline{x}_{m}+\sum_{m=1 }^{N_{x}N_{y}}\widetilde{n}_{tm}\overline{x}_{m} \tag{4}\]  
For fixed pixel with index \(m\):  
\[k_{tm}=\alpha_{t1}l_{1m}+\alpha_{t2}l_{2m}+\alpha_{t3}l_{3m}+\ldots+\alpha_{ tN_{p}}l_{N_{p}m}+\widetilde{n}_{tm}=\sum_{j=1}^{N_{p}}\alpha_{tj}l_{jm}+ \widetilde{n}_{tm}. \tag{5}\]  
If we assume that pixel \(m\) only contains one archetype (\(N_{p}=1\)), then  
\[k_{tm}=\alpha_{t}l_{m}+\widetilde{n}_{tm}, \tag{6}\]  
where \(\{l\}\) determine the archetype image, and \(\alpha_{t}\) determines overall brightness modulation of that archetype. Thus, in Expression (6) brightness of one particular pixel \(k_{tm}\) is connected to brightnesses of the only archetype present in that point (\(\alpha_{t}\)). Expression (6) allows for proposing the following principle for archetype image extraction. If an image stack is sorted in such a way that \(m\)-th pixel changes its brightness (\(k_{tm}\)) according to a certain function (e.g., harmonically), then the brightness of an entire archetype owning that pixel (\(\alpha_{t}\)) will change according to the same function. Thus, the following procedure can be proposed (schematically presented in Figure 6):  
* Collect a stack of raw thermographic images and convert this stack to a 2D array
* Apply SVD to extract principal components. * Leave only meaningful PCs (those which determine most of variance of raw data). * Choose point(s) where only one independent pattern is present (e.g., only the pattern describing the defective area). These points are selected manually as it is important to find the point where only one independent pattern is present and thus Expression (6) is satisfied. * Create a large number of random linear combinations from the PCs extracted. For research presented in this article, a set of 18,000 random combinations was constructed. * Sort the new stack of images in such a way that the brightness of pixels in point(s) chosen change in a harmonic way. * Find all pixels which happen to have similar modulation. These pixels belong to the same archetype which is present in the point(s) chosen.
# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 4 Results and Analyses
### Comparative Analyses
#### 4.3.3 Feature Extraction and Model Performance
Consequently, while ViT exhibited robust overall accuracy, the highest accuracy was achieved by the 1D-CNN model with MFCCs + \(\Delta\)MFCCs, which excelled in capturing intricate age-related features specific to Korean speech data. This finding suggests that, while transformers are beneficial for tasks requiring broad contextual analysis, CNNs are particularly effective when nuanced, localized feature extraction is crucial for accuracy. In summary, the MFCCs + \(\Delta\)MFCCs and 1D-CNN combination proved to be the most effective for predicting the age of Korean speakers. In addition, the ViT model demonstrated high performance, even in cases where feature identification was more difficult, demonstrating its potential for application in speech recognition tasks.
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 5 Results
### Security Settings
#### 5.4.1. Expiration Time  
Selected:  
For the employee form, 14 out of 19 participants stated that they would select the expiration time feature. When we asked why they would select this option, 13 participants stated that it would be sufficient access time for the scenario. Only one participant mentioned that he would choose this option to avoid access afterward. For the W-9 form, 13 participants mentioned selecting an expiration time to remove continued access. Only one participant would select this because of the urgency of document access. For the medical form, 16 participants selected this feature to allow access for limited time.  
Participant P7 commented: _\"I would like it to disappear since it is a 9 W form that I'm sending. Once it has been looked at by the accountant. I would like it not to still be in the email. So that my people aren't able to access it after those 24 h, and it's not just sitting there because, you know, people don't always usually go back and delete all their emails. So it just kind of sits there. So, having this expire in a day, would be like really convenient.\"_  
Not Selected:  
Five of our participants did not select the expiration time option for the employee form, and they would do so because the company may need future access. One participant mentioned avoiding a bad impression, and another participant shared that it was not important to him. For the W-9 form, three participants chose not to select the expiration time to allow future access. Also, one participant mentioned that it was not needed for that document. However, for the medical form, only two participants stated that this feature was not needed.  
Participant P6 commented: _\"Because even though they said it would last for a week. I don't know how fast or how slow HR works, and so I don't want to set a bad impression by sending them something, and then, once they get around accessing it, they find that it's already expired and gone.\"_
# New Landslide Disaster Monitoring System: Case Study of Pingding Village
## 3 Results
### Monitoring Data Analysis
#### 3.2.3 Monitoring Data of the Retractable Meters  
The monitoring data from the magnetic induction and cable retractable meters during Typhoon Sinlaku, which are shown in Figure 16, were compared with the management values. Except for the micro-displacements caused by the indium steel wires of the cable retractable meters, all of the retractable meters remained in a stable condition and the set threshold of the vigilance values was not exceeded.
## 2 Mathematical Modeling
The boundary conditions for the above mathematical model are (see [25]):  
\[u=U(x,t  
for the above problem the transformed boundary conditions are:  
\[f(0)=f_{w},\] \[f^{\prime}(0)=1+S_{f}f^{\prime\prime}(0), \tag{13}\] \[\theta(0)=1+S_{\theta}\theta^{\prime}(0),\] (14) \[\phi(0)=1+S_{\phi}\phi^{\prime}(0),\] (15) \[\xi(0)=1+S_{\xi}\xi^{\prime}(0),\] (16) \[f^{\prime}(\infty)\to 0,\theta(\infty)\to 0,\phi(\infty)\to 0,\xi(\infty)\to 0, \tag{17}\]  
where unsteadiness parameter \(\sigma=\lambda/a\), \(M\) is the magnetic field parameter, \(Pr\) is the Prandtl number, \(Nb\) is the Brownian motion parameter, \(Nt\) is the thermo-phoresis parameter, \(\beta\) is the Deborah number, \(Sc\) is the Schmidt number, \(R\) is the thermal radiation parameter, \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) are the buoyancy parameters, \(k_{p}\) is the permeability parameter, \(Nd\) is the Dufour parameter, \(Sr\) is the Soret parameter, and \(f_{w}\) is the Suction/injection parameter. The primes show the differentiation with respect to \(\eta\). The parameters used in Equations (8)-(11) are explained as:  
\(M=[\frac{\sigma(1-\lambda t)}{\rho a}B_{0}]^{1/2}\), \(Pr=\
u/\alpha\), \(Nb=\tau D_{B}(C_{w}-C_{\infty})/\
u\), \(Nt=(\tau D_{T}(T_{w}-T_{\infty})/\
u T_{\infty}\), \(\beta=k_{0}a/(1-\lambda t),Sc=\frac{\
u}{D_{w}}\), \(Le=\frac{\sigma}{D_{w}}\), \(R=(16\sigma^{*}T_{\infty}^{3})/(3k^{*}K)\), \(\sigma=\lambda/a\), \(\lambda_{1}=(g\beta_{T}T_{0})/(2 av)\), \(\lambda_{2}=(g\beta_{C}C_{0})/(2 av)\), \(\lambda_{3}=(g\beta_{E}E_{0})(2 av)\), \(Sr=(D_{T}T_{0})/\
u C_{0},N_{d}=(D_{m}K_{T}C_{0})/(C_{s}C_{p}T_{0})\), \(k_{p}=\
u(1-\lambda t)/a\), \(f_{w}=-v_{w}(1-\gamma t)^{1/2}/(va)^{1/2}\),  
Also, the local skin friction co-efficient, local Nusselt number, and local Sherwood number are explained as below  
\[C_{f}=\frac{\mu}{\rho(U_{w})^{2}}(\frac{\partial u}{\partial y})_{y=0} \tag{18}\]  
\[N_{u}=\frac{x}{k(T-T_{\infty})}[k(\frac{\partial T}{\partial y})_{y=0}-\frac {4\sigma*}{3k*}(\frac{\partial T^{4}}{\partial y})_{y=0}] \tag{19}\]  
\[Sh=-\frac{x}{(C-C_{\infty})}(\frac{C}{\partial y})_{y=0} \tag{20}\]  
when Equation (7) is substititted into Equations (17)-(19), the final dimensionless form is obtained; \(C_{fr}=(Re_{x})^{1/2}=f^{\prime\prime}(0)\), \(N_{ur}=N_{u}/(Re_{x})^{1/2}=-(1+R)\theta^{\prime}(0)\), \(Shr=Sh/(Re_{x})^{1/2}=-\phi^{\prime}(0)\) where the local Reynolds number is \(Re_{x}\), reduced skin friction is \(Crr\), reduced Nusselt number is \(Nur\), and the reduced Sherwood number is \(Shr\). Figure 1: Physical sketch with xy-coordinates.
# A Novel Approach for the Estimation of Doubly Spread Acoustic Channels Based on Wavelet Transform
###### Abstract  
In this paper, the estimation of doubly spread underwater acoustic (UWA) channels is investigated. The UWA channels are characterized by severe delay spread and significant Doppler effects, and can be well modeled as a multi-scale multi-lag (MSML) channel. Furthermore, exploiting the sparsity of UWA channels, MSML channel estimation can be transformed into the estimation of parameter sets (amplitude, Doppler scale factor, time delay). Based on this, the orthogonal matching pursuit (OMP) algorithm has been widely used. However, the estimation accuracy of OMP depends on the size of the dictionary, which is related with both delay spread and Doppler spread. Thus it requires high computational complexity. This paper proposes a new method, called wavelet transform (WT) based algorithm, for the UWA channel estimation. Different from OMP algorithm which needs to search in both time domain and Doppler domain, WT-based algorithm only needs to search in time domain by using the Doppler invariant characteristic of hyperbolic frequency modulation (HFM) signal. The performance of the proposed algorithm is evaluated by computer simulations based on BELLHOP. The simulation results show that WT-based algorithm performs slightly better than OMP algorithm in low signal to noise ratio (SNR) while can greatly reduce computational complexity.  
underwater acoustic (UWA) communication; multi-scale multi-lag (MSML) channel; wavelet transform (WT) based algorithm; hyperbolic frequency modulation (HFM) signal Article
# Research on Flexible Traceability System of _Agaricus bisporus_ Supply Chain
###### Abstract  
The supply chain of _Agaricus bisporus_ constitutes a complex, multi-layered production and distribution network, encompassing various stages. Challenges in information transmission and process integration between these stages often lead to reduced query efficiency, subsequently compromising the credibility of traceability results and the flexibility of the system. In traditional traceability systems, data for multiple service recipients may be stored on a single blockchain, resulting in complex data structures and making it challenging for users to conduct effective queries and analyses. To address this issue, this paper proposes a solution: a multi-chain tracing model tailored for stakeholders in the traceability process of _Agaricus bisporus_. This model establishes public chains, enterprise chains, and supervision chains for consumers, enterprises, and regulatory authorities, respectively, to enhance system storage and query efficiency. Additionally, leveraging the modular design of Hyperledger Fabric, the paper optimizes and upgrades the supply chain of _Agaricus bisporus_. The proposed multi-chain tracing model, designed for traceability for stakeholders, undergoes rigorous validation. Analysis and application results demonstrate that this approach significantly improves the query efficiency and credibility of traceability information while markedly enhancing system flexibility.  
blockchain; flexible traceability; multi-chain model; Hyperledger Fabric; _Agaricus bisporus_ traceability
## 1 Introduction
### Objectives  
In this study, the dimension of the reinforced concrete protection wall was taken from the Korea gas safety code book [16], and various numerical simulation analysis was considered. The primary purpose of this study is to compare a reinforced concrete and composite structure protection wall for hydrogen gas storage stations to save from serious harm to people in the region of the accident and residential areas under explosion. In the code book, minimum height and foundation dimensions are provided but the length of the wall is missing. The code book does not explain the distance between the hydrogen gas tank and the protection wall. Researcher studied the prismatic 5.27 m\({}^{3}\) volume that contained 30% hydrogen and 70% air with a thickness of the protection wall 0.15 m, and the distance of the hydrogen gas prismatic was 2 m and 4 m. Even though the volume of the prismatic is 5.27 m\({}^{3}\), and the distance is 2 m and 4 m, cracks formed after the explosion on the protection barrier [8]. If the storage tank volume increases, the protection wall gets more damage, and concrete fragmentation reaches a far distance. In KGS FP217, the minimum thickness required for the protection wall is 0.12 m. So, it is necessary to study the protection wall under hydrogen explosion through numerical simulation. Figure 1a shows the hydrogen tank explosion that occurred in Gangneung, Republic of Korea, on May 2019. There was no secondary fire, and the detonation pressure merely caused the damage, but within a 15-m distance from the hydrogen tank, the structure beam of the building was damaged, and most of the windows of the 5-story building, which is about 100 m away and faced to the hydrogen tank, are all crushed. The equivalent TNT is estimated to be about 50 kg [17]. Figure 1b shows the site visit at the hydrogen station at Samcheok, Republic of Korea. The thickness of the reinforced concrete is 0.35 m in Figure 1b, but the distance between the hydrogen gas trailer and the protection wall is 1.53 m. The distance between the protection wall and hydrogen gas storage follows the International Fire Code (IFC) 2009 (States, 2007) [18]. Figure 1a shows the damage on hydrogen storage site after the explosion of hydrogen storage tank. Figure 1b shows the current practice of barrier wall for hydrogen gas station at samcheok, Republic of Korea during site visit and Table 1 represents the minimum requirement of barriers wall thickness in different countries. It is necessary to understand the minimum requirement of barrier wall as in KGS FP217 is safe or not during hydrogen gas explosion. To understand the damage on protection wall under hydrogen gas explosion worse-case scenario (300 kg of Hydrogen gas) and 30 kg of hydrogen gas explosion was carried out through TNT equivalent method and simulation was done with LS-DYNA. Very limited research has been done on hydrogen gas explosions nearby barrier walls.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 3 Proposed Update Methods
### Only Unicast Method  
The update process is governed by some variables stored by the node: the first one (_bin_flags_) represents the number of received fragments of the binary file. Another variable (_check_flags_) stores the number of received fragments of the checksum. It must be 0 until all the fragments of the binary file have been received. Finally, another variable stores the current firmware version.  
These are the stages of the update (see Figure 1):  
(a) _Initial stage_. The update starts when the gateway sends an _OTA RESET REQUIST_. The node then sets both counters to 0 and answers with an _OTA RESET CONFIRMED_, which includes its current firmware version. If the gateway does not receive an answer, a timeout triggers a new _OTA RESET REQUIST_. Once the gateway receives the confirmation, it checks that the firmware version running at the node is different from the one that is going to be transmitted and flashed.  
(b) _Sending of the binary_. This uses _OTA BINARY FRAGMENT_ frames, which include an identifier (_FRAGMENT NUMBER_). The gateway sends the fragments, following a \"stop and wait\" policy: if a fragment with the incorrect identifier arrives, it is discarded and a _WAITING FOR BINARY FRAGMENT_ message is sent by the node, specifying the expected fragment. When sending the last fragment, the gateway uses a special message called _OTA BINARY LAST FRAGMENT_. After that, it sends an _OTA STATUS REQUEST_, and the node answers with an _OTA STATUS RESPONSE_, including the field _bin_flags_ (number of binary fragments).  
(c) _Sending of the checksum_. If the number of binary fragments reported by the node is correct, the gateway starts a similar procedure for sending the checksum, which may require one or more frames. The last fragment of the checksum is sent inside an _OTA CHECKSUM LAST FRAGMENT_ frame. This stage concludes when the node sends an _OTA STATUS RESPONSE_ frame with the correct value of _check_flags_ (number of checksum fragments).  
(d) _Checksum validation and firmware flashing_. The gateway waits a time interval to let the node calculate the checksum. Then, it sends a new _OTA STATUS REQUEST_. The nodeanswers with an _OTA STATUS RESPONSE_. If everything is correct, the gateway sends an _OTA FLASH NEW VERSION_ order. The node flashes the firmware it has stored and reboots. If the checksum is not correct, the procedure starts again.  
(e) _Final confirmation_. The gateway waits a time lapse to let the node flash and reboot. Finally, once the node has associated again, it sends an _OTA STATUS REQUEST_ frame. In the response, the node includes its firmware version, which should be the new one.
## 6 Data Analysis
### Analysis of General Data  
The review included 74 papers from various journals and conference proceedings indexed in five electronic databases. The percentage of papers published from 2015 to 2022 is shown in Figure 5. According to the review results, research on security policy techniques in the BYOD environment only gained popularity in 2017. Fifteen percent and 17 percent of all publications in 2017 and 2018, respectively, were found in journals. Journal articles accounted for 17 percent of all publications identified from review efforts in 2020. From 2021 to December 2022, this rose to 20 percent, suggesting more researchers had become interested in the field. Figure 6 illustrates the distribution of publications throughout the various databases covering a wide range of subjects. WOS provided the majority of the articles, followed by the IEEE database. Scopus was the second most popular database, followed by Science Direct and Wiley, with the lowest percentages.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Inclusion** & **Exclusion** \\ \hline
**IC1:** Papers related to research questions. & **EC1:** The papers do not address security policies based on BYOD. \\
**IC2:** Papers from journals or conferences. & **EC2:** Techniques and models used in the security policy are not addressed. \\
**IC3:** Papers are written in English only. & **EC3:** Duplicate papers. \\
**IC4:** Papers published between 2015 and 2022. & **EC4:** Full text is unavailable. \\
**IC5:** The full text is available. & **EC5:** Non-English. \\
**IC6:** Articles that present techniques and models of security policy in a BYOD environment. & **EC6:** White papers, chapters in books and magazines. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Inclusion and exclusion criteria.
## 4 Discussion
### Areas where NDVI Increased but WUE Decreased  
The results show that, from 2001 to 2010 and 2011 to 2020, the vegetation coverage in the Loess Plateau increased by 13.08% and 8.8%, but the vegetation WUE decreased. The distribution areas were mainly located in Shanxi and Shaanxi, among which the most obvious impact was seen in meadows and woodlands [33].  
Due to the relatively high average temperature in the eastern part of the Loess Plateau, with the increase in temperature and light, vegetation transpiration has increased, and soil water evaporation has intensified. Constrained by water, precipitation has become the main limiting factor [34]. However, large-scale vegetation growth requires more water, and precipitation cannot replenish soil water loss in time, resulting in a decline in regional vegetation WUE [35]. From 2001 to 2010, when the policy of returning farmland to forest was implemented, large-scale artificial aforestation led to a significant increase in evapotranspiration at a rate of 6.33 mm per year, but precipitation in the region decreased slightly at a rate of 0.028 mm per year, thus becoming a major factor for the decline in regional vegetation WUE. In 2010-2020, compared with 2001-2010, the area of increased WUE decreased by 4.28%, and the overall distribution moved from the south to the east. This is because some grasslands were transformed into cultivated land after 2010, and artificial irrigation also provided a certain amount of water for the surrounding vegetation, which alleviated this phenomenon to a certain extent.
## 2 Related Work
### LSTM Model  
In traditional neural networks, each neuron is independent and unaffected by time series. In RNNs, time series data are used as input [24]. Earlier layers of an RNN exert weaker effects than subsequent decisions. When too many series are present in the data, the gradient disappears or explodes. To address this problem, [PERSON] and [PERSON] proposed the LSTM model [39] in 1997. An LSTM model comprises numerous LSTM cells, each having three inputs, three components, and two outputs. The three inputs \(x_{t}\) are the input at time \(t\), the output \(h_{t-1}\) at time \(t-1\), and the long-term memory (LTM) \(c_{t-1}\) at time \(t-1\). The three components are the input gate \(i_{t}\), the output gate \(o_{t}\) and the forget gate \(f_{1}\). The three components all use sigmoid functions as activation functions to obtain an output value between 0 and 1, simulating the opening and closing of a valve. The input gate uses the input \(x_{t}\) at time \(t\) and the output \(h_{t-1}\) at time \(t-1\) to determine whether the LTM \(C_{t}\) should incorporate the memory \(\dot{C}_{t}\) generated at time \(t\). The output gate determines whether the LTM \(C_{t}\) generated at time \(t\) should be output according to the input \(x_{t}\) at time \(t\) and the output \(h_{t-1}\) at time \(t-1\). The forget gate uses the input \(x_{t}\) at time \(t\) and the output \(h_{t-1}\) at time \(t-1\) to determine whether the LTM \(C_{t-1}\) at time \(t-1\) should be added to the LTM \(C_{t}\) at time \(t\). The two outputs of the LSTM model are the output \(h_{t}\) and the LTM \(C_{t}\) at time \(t\). The LSTM model has one more output (\(C_{t}\), or LTM) than ordinary RNNs do, which enables it to solve the gradient problem caused by excessive time series in ordinary RNNs.