esa-sceva/satcom-chunk-collection huggingface dataset:
# Policy-Compliant Maximum Network Flows
## 3 Graph Model and Basic Algorithms
### Residual Graphs and Augmenting Paths  
One line of solving this so-called maximum flow problem is via the introduction of residual graphs and the calculation of augmenting paths. In short, we iteratively increase the value of the flow, starting with \(\forall u,v\in V:f(u,v)=0\). At each step, we calculate an augmenting path in the residual graph, which is nothing but the original graph, extended with return-edges. We will now formally define the necessary concepts.  
Figure 1: It doesn’t matter for oil whether it followed path abc, path def or path dgc, but in this paper, we do care about the specific paths followed. Policies about which path is acceptable and which path is not, are expressed via constraints on the labels across the paths, i.e., abc, def and dgc.  
First, we define the residual capacity \(c_{f}:V\times V\to\mathbb{R}\) in the following way. We iterate over all edges \((u,v)\in E\) and for each such edge we define the following:  
\[\begin{cases}c_{f}(u,v)=c(u,v)-f(u,v)\\ c_{f}(v,u)=f(u,v)\end{cases} \tag{3}\]  
If there is no edge between \(u\) and \(v\) in any direction, i.e., \((u,v)\
otin E\) and \((v,u)\
otin E\), then both \(c_{f}(u,v)=0\) and \(c_{f}(v,u)=0\).  
Next, we define the residual network \(G_{f}=(V,E_{f})\), where \(E_{f}=\{(u,v)\in V\times V:c_{f}(u,v)>0\}\). So, for each edge in the original graph we have a residual edge if the capacity of that edge is not filled up yet, and moreover we add a reverse edge when it carries any flow at all.  
This residual graph will now be used to find augmenting paths. Given a flow network \(G=(V,E)\) and a flow \(f\), an augmenting path \(p\) is defined as a path from \(s\) to \(t\) in the residual network, that is \(p=[e_{1},e_{2},\ldots,e_{n}]\), such that  
* edges belong to \(E_{f}\), thus \[\forall e_{i}:e_{i}\in E_{f},\] (4)
* we start in \(s\), thus \[\exists v:e_{1}=(s,v),\] (5)
* succeeding edges are connected, thus \[\forall i\in\{1,\ldots,n-1\}:\exists u,v,w:e_{i}=(u,v)\wedge e_{i+1}=(v,w)\] (6)
* we end in \(t\), thus \[\exists v:e_{n}=(v,t).\] (7)  
In order to increase the flow \(f\), we will change the flow along the edges of the augmenting path \(p\) with the maximum amount possible, namely the residual capacity of the path \(p\) which is  
\[c_{f}(p)=min\{c_{f}(u,v):(u,v)\in p\}. \tag{8}\]  
The flow \(f\) can now be increased to a new flow \(f^{\prime}\) as follows:  
\[f^{\prime}(u,v)=\begin{cases}f(u,v)+c_{f}(p)&\text{if }(u,v)\in p,\\ f(u,v)-c_{f}(p)&\text{if }(v,u)\in p,\\ f(u,v)&\text{otherwise.}\end{cases} \tag{9}\]  
Iteratively finding augmenting paths in a residual graph and increasing the flow will finally come to a stop when there are no augmenting paths anymore. Then, the maximum flow has been reached. Depending on the procedure used to find augmenting paths, the above algorithm is called Ford-Fulkerson or Edmonds-Karp.
# Mathematical Foundations and Implementation of CONIKS Key Transparency
## 2 Related Work
### Security Assumptions and Models
#### 2.3.3 Algorithms and Data Structures  
The core algorithms and data structures for implementing CONIKS include Merkle trees for key transparency and digital signature algorithms for authentication. The Merkle tree is constructed using a binary tree structure where each internal node is a hash of its child nodes. For this, a class-based implementation in Python utilizes binary trees, where each node stores hash values and provides methods for insertion and hash computation. The _MerkleTree_ class handles the construction and updating of the tree, while the _HashNode_ class represents each node in the tree.  
Digital signatures are implemented using RSA or ECDSA algorithms, depending on the chosen cryptographic method. The _cryptography_ library provides methods for generating keys, signing messages, and verifying signatures. Key management involves the secure generation and storage of public and private keys, ensuring that they are used correctly for signing and verification processes.
## 4 Conclusions  
The demonstrated approach for the detection and classification of shifts in vegetation activity trends is the first to consistently assess different types of changes on the biosphere during the past decades. The availability of third generation 1982-2011 Global Inventory Modeling and Mapping Studies (GIMMS) data was crucial for this study. The analysis provided new insights to several aspects of global vegetation dynamics:  
(1) Monotonicity was found for 26 percent of the analyzed 0.083\({}^{\circ}\) grid cells with a significant temporal trend. Shifts in vegetation activity trends were found for the remaining land surface: either an interruption (greening with \"setback\", respectively browning with \"burst\") or a reversal (greening-to-browning or _v.v._). Setbacks were found in many greening trends, in particular in western India, Asia Minor and Western Australia. Bursts in browning trends were conspicuous in the Horn of Africa and in northern Patagonia (Rio Negro province).  
(2) Over time, an increase in the frequency of trend shifts was found. With respect to the 1980s, a four times larger area with changing trends was found in the 2000s. This suggests that relatively stable vegetation regimes have changed towards higher turnover rates, implying an increase in the complexity of biospheric alterations and responses to external forces.  
(3) Greening trends were found to have reversed into browning more than the other way around, which indicates a decline in vegetation activity. Compared with the 1980s, negative vegetation activity trends were found to have increased in area about 40% over time. Most of the greening-to-browning reversals occurred around the millennium transition. They were not confined to a single geographical region, but extended across all continents. Notable regions include parts of southern Argentina, northern Kazakhstan, the Middle East, the Sahel, Eastern Africa and Western Australia. The other way around, browning-to-greening reversals were found in central Australia and northern Argentina. These mainly occurred in recent times (late 2000s) and may therefore represent short-lived processes.  
Further analysis is needed to attribute observed trend changes to cause-effect relationships, as well as validating these trend changes using evidence from climatic, economic, and other human impact data alike.
## 4 Experiment and Analysis
### Analysis of Experiments
#### 4.2.1 Repeatability and Reliability of Experiments  
Table 3 shows the avg and SD for both the motorcycle speed (\(v\)) and roll angle gain (RA gain \(a\), for the positive value regions) for each experiment. The avg and SD were calculated for three professional riders who have ridden the motorcycle at target speeds of 3, 5 and 10 km/h. Each rider repeated the experiments two times (named as'set' in the table). The table shows that the avg and SD of the motorcycle speeds and the roll angle gains match closely when the same rider repeated the experiment. This ensures the repeatability of the experiments.  
The avg and SD of the RA gain increased sharply when the target speed reduced from 5 to 3 km/h than the same from 10 to 5 km/h; although, the SD of the speeds were similar. This was due to the fact that the effort and correction in steering input required to attain the low-speed stability increase sharply as speed reduces. The avg and SD in the RA gains were similar for all the rider at the particular target speed, which ensured the reliability of the experiments.
## 3 Results and Discussion  
### Key Cannabinoids  
There are increasing pieces of evidence that the medicinal properties of _Cannabis_ mainly come from cannabinoids [39]. In living and raw _Cannabis_ plants, both cannabigerolic acid (CBGA) and cannabigevarolic acid (CBGVA) are found to be the primary metabolite precursors that can be converted into smaller and more stable cannabinoid acids and cannabinoids by enzyme reactions, decarboxylation by heating or smoking, and oxidation by light and air exposures [25; 40].  
As shown in Figure 2, through enzyme reactions, CBGA turns into tetrahydroconnabi-nolic acid (THCA), cannabidiolic acid (CBDA), and cannabichromeanic acid (CBCA), whereas CBGVA breaks down into cannabichromevarinic acid (CBCVA), cannabidivarinic acid (CBDVA), and tetrahydroconnabivarinic acid (THCVA); by decarboxylation, CBGA becomes cannabigerol (CBG), THCA becomes \(\Delta^{9}\)-tetrahydrocannabinol (\(\Delta^{9}\)-THC), CBDA becomes cannabidiol (CBD), CBCA becomes cannabichromeene (CBC), CBGVA becomes cannabigerivarin (CBGV), CBCVA becomes cannabichromevarin (CBCV), CBDVA becomes cannabidivarin (CBDV), and THCVA becomes \(\Delta^{9}\)-tetrahydrocannabinolic acid (\(\Delta^{9}\)-THCA). Furthermore, \(\Delta^{9}\)-THC can be converted to cannabinol (CBN) by oxidation and \(\Delta^{8}\)-tetrahydrocannabinol (\(\Delta^{8}\)-THC) by isomerization, as well as 11-Hydroxy-\(\Delta^{9}\)-tetrahydrocannabinol (11-OH-\(\Delta^{9}\)-THC) by metabolization. The acidic and neutral forms of phyto-cannabinoidss can undergo further transformation to generate over one hundred different cannabinoidss.  
Among the cannabinoids discovered, the psychoactive \(\Delta^{9}\)-THC and the non-psychoactive CBD are the two most abundant cannabinoids, and all other cannabinoidss are in trace amounts. The health effects of twelve key cannabinoids including \(\Delta^{9}\)-THC, \(\Delta^{8}\)-THC, 11-OH-\(\Delta^{9}\)-THC, \(\Delta^{9}\)-THCA, \(\Delta^{9}\)-THCV, CBN, CBD, CBDA, CBDV, CBC, CBG, and CBGA have been investigated, the therapeutic benefits of these cannabinoidss range from analgesics, anorectic, anxiolytic, appetite stimulant, anti-bacterial, anticonvulsant, anti-diabetic, anti-epileptic, anti-fungal, anti-inflammatory, anti-insomnia, anti-ischemic, anti-proliferative, antiposoriatic, antipsychotic, and antispasmodic to bone-stimulant, immunosuppressive, intestinal antiprokinetic, and neuroprotective, etc. [39]. Hence, these cannabinoidss were adopted as the chemical markers of the _Cannabis_ potency test (expect 11-OH-\(\Delta^{9}\)-THC) (Medical Marijuana SCF Technical Guide, Version 5.0, Available at [[https://www.michigan.gov/documents/lara/FINALTESTING.GUIDE_634575_7.pdf](https://www.michigan.gov/documents/lara/FINALTESTING.GUIDE_634575_7.pdf)]([https://www.michigan.gov/documents/lara/FINALTESTING.GUIDE_634575_7.pdf](https://www.michigan.gov/documents/lara/FINALTESTING.GUIDE_634575_7.pdf)), accessed on 4 October 2021).
## 3 DT Security and Reliability
They can lead to the loss of one or more shares of data, inconsistencies between different copies of the same data, and/or the inability to recover the original data. Collusion threats are illegal agreements between two or more adversaries (in the context of different paths between nodes, the adversaries are nodes) to gain full access to personal data. Cryptographic protocols can be used to mitigate the risks of deliberate threats, but this is not enough for random threats. We consider reliability and security as close concepts of an information violation. Therefore, statements related to reliability are used to discuss security and vice versa. To improve the security and reliability of data transmission systems, DT is based on access structures and error correction codes. It transmits data through various paths between nodes and minimizes the chance of information theft or loss in case of intentional and accidental threats. In the next sections, we show how the size of shares and their number can change the reliability, security, speed, etc. of data transmission. These structures reduce the load on the transmission network compared to the classical replication mechanism and reduce the cost.
# The Task of Post-Editing Machine Translation for the Low-Resource Language
## 3 Results  
The architecture of the post-editing information system for English-Kazakh and Russian-Kazakh machine translation consists of three main modules, each comprising subtasks and data:  
* Client-interface module;
* Post-edit module;
* NL resource module.  
The prototype of the post-editing system consists of these three modules. The operational scheme of the post-editing system prototype is illustrated in the Figure 5 below.  
The first module, the client-interface module, serves as the user interface connecting the user with the system. This module includes the following:  
* Input windows for processing and editing functions linked to the post-edit module.
* Output result windows.  
To process the input data using trained machine translation and post-editing models, a server is created, which calls the specified models upon request. As the experimental part involved training with the open-source framework OpenNMT, its server configuration is used for model invocation.  
The subsequent steps involve the stages of post-editing. The post-edit module, the core component of the system, consists of the following levels (sub-modules): light and full post-editing, which include the following processes:  
* Processing level: this level involves linguistic and statistical processing of input data, removing stop words, correcting the input text, normalization, stemming, identifying incorrect words, resolving abbreviations, and proper noun resolution;
* Structural level: this level addresses grammatical errors in the text, determining the correct syntactic structure of simple and complex sentences in the Kazakh language;
* Edit level: at this level, semantic analysis of the text is performed, resolving word ambiguity, possible substitutions, and text editing.  
In the software implementation, post-editing is carried out using both light and full post-editing modules and a rule-based approach. This approach allows for working with both simple and complex sentences [25].
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Year** & **Focused Key Areas** & **Challenges Addressed** \\ \hline
[29] & 2017 & A comprehensive review of UOWC principles, channel modelling, and applications & Channel fading, high BER, and deployment complexities \\ \hline
[30] & 2018 & Survey of underwater sensor networks’ software-defined techniques, highlighting flexibility in network management & Interoperability and energy constraints \\ \hline
[31] & 2019 & Survey of a framework for integrating UOWC into underwater networks and localization in UOWC & Networking challenges and localization accuracy \\ \hline
[32] & 2019 & Tutorial on detailed analysis of why UOWC is essential, fundamental concepts, key challenges, and directions & Limited range, energy efficiency, and signal attenuation \\ \hline
[33] & 2020 & Systematic review of recent advancements, main challenges, recent experimental setups, and future directions in UOWC & Practical deployment and environmental effects \\ \hline
[34] & 2020 & Overview of UOWC architecture, potential applications, and future research guidance & Cost, energy consumption, and system scalability \\ \hline
[35] & 2021 & Overview of UOWC lighting system design, link configurations, important communication techniques, light propagation impairments, and channel modulation methods & Biofouling, deployment costs, and optical signal interference \\ \hline
[36] & 2022 & Survey of impairments, security issues, and practical solutions & Security threats and environmental effects on signal quality \\ \hline
[37] & 2023 & Survey of different UOWC systems and recent works on signal processing methods & Signal degradation and data rate enhancement challenges \\ \hline
[38] & 2024 & Perspectives on UOWC links, analysis of current challenges and trends in UOWC research, and channel modulation techniques & Deployment costs, turbulence effects, and system reliability \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison of the latest existing UOWC-related literature. Figure 1: Main review topics.
## 5 Results and Discussion
### Full Set versus Spectral Set versus Stability Set  
The classification results for the spectral set (\(H_{1-4}\) + SE) and the stability set (\(J_{r}+S_{r}\)) are shown in Table 4 and Figure 8. Compared with the full set (80.1%), the spectral set achieved a lower, but still satisfactory overall accuracy (75.1%), while the stability set performance was unsatisfactory, as its maximal accuracy was only 61.5%. The addition of stability features into the spectral set for classification yielded an observable improvement of 5% in overall accuracy. The overall accuracies of the spectral set were much greater (15.6% on average) than those of the stability set. The breathy and the pressed voice classification in Figure 8 show that the spectral set yielded greater accuracies than the stability set for all classifiers. The pressed voice was totally indistinguishable using the stability set, as the average accuracy was below 30%. However, the modal voice classification results showed that the stability set had a greater average accuracy (6.8% greater) than the spectral set. This means that the modal voice was more steady than the other two voice types in vocal intensity and fundamental frequency. The LD achieved a very high accuracy (86.0%) for the modal voice classification using the stability set. For breathy and pressed voice classification, all five classifiers achieved a comparable accuracy (83.0% \(\pm\) 1.2%, 74.0% \(\pm\) 1.6%) using the spectral set. However, the similar distribution between the breathy and the pressed samples in jitter and shimmer indicated a high possibility of misclassification between these two types using the stability set in Figure 5b.
## 2 System Modeling and Optimization Problems
### Modeling Optimization Problems  
Based on the communication system model shown in Figure 1, this section maximizes the system energy efficiency under the BS transmit power and IRS phase constraints by jointly designing the BS transmit beamforming and the IRS phase shift. The IRS-assisted energy efficiency optimization problem for HST communication systems is modeled:  
\[\begin{split}\text{P1}:\max_{f,\mathbf{\Theta}}\frac{R}{P_{all}}= \frac{\log\left(1+\frac{\left|\left(h^{H}\mathbf{\Theta}G+h_{d}^{H}\right)\mathbf{f} \right|^{2}}{\xi^{2}}\right)}{\frac{1}{\xi}{\left\|\mathbf{f}\right\|}^{2}+P_{BS}+ NP_{R}+P_{MR}}\\ \text{s.t.}\ \text{C1}:\left\|\mathbf{f}\right\|^{2}\leq P_{\max}\\ \text{C2}:\left|v_{n}\right|=1\quad\forall n=1,\cdots,\text{N} \end{split} \tag{12}\]  
where \(\left\|\cdot\right\|\) denotes the Euclidean Paradigm operator. In the context of the optimization problem P1, where \(P_{\max}\) represents the BS transmit power, C1 represents the maximum BS transmit power constraint, and C2 represents the IRS unit modulus constraint. Solving the problem becomes challenging due to the strong coupling between variables \(f\) and \(\mathbf{\Theta}\), while also satisfying the constraint C2 on the IRS phase shifts. It is a continuous optimization problem [33]. To address this issue, we adopt an alternating optimization strategy to decouple the variables. Specifically, we decompose problem P1 into two sub-problems: the BS transmit beamforming sub-problem and the IRS phase-shift optimization sub-problem. By iteratively solving these two sub-problems in an alternating manner, we achieve convergence.
# Target Speaker Extraction by Fusing Voiceprint Features
## 2 Methodology
### Overview of Tse-FV
#### 2.1.1 Feature Extraction  
Given a noisy audio containing different speakers' speech \(\mathbf{a}_{mix}\), the magnitude spectrograms can be extracted as follows. First, \(\mathbf{a}_{mix}\) is transformed into the complex domain using an STFT. Then, the phase spectrograms \(\mathbf{x}_{phase}\) and the magnitude spectrograms \(\mathbf{x}_{mag}\) are obtained. \(\mathbf{x}_{mag}\) was adopted to generate the masked magnitude spectrograms corresponding to the target speaker according to the scheme used in [6; 8; 9].  
Given a reference speech \(\mathbf{s}\) of the target speaker, two types of voiceprint features are extracted (as shown in Figure 1a). The first type is the MFCC features \(\mathbf{r}_{mfc}\). When calculating the mel spectrograms, the lengths of the fast Fourier transform (FFT) window, the frame shift, and the number of mel filters are set to 256, 64, and 40, respectively. The dimension of the extracted MFCC features is set to 13. In the end, a set of MFCC features \(\mathbf{r}_{mfc}\in\mathbb{R}^{T\times 13}\), where \(T\) denotes the number of time frames, can be obtained.  
The second type of voiceprint features is the d-vector. The d-vector extraction model, which consists of three LSTM layers and a linear layer, is trained on the VoxCeleb1 [34] dataset with the generalized end-to-end loss function [26]. The d-vector is computed based on the 40-dimension log-mel spectrograms of the reference speech \(\mathbf{s}\). An L2 normalization is performed on the 256-dimension output of the d-vector extraction model to obtain the d-vector features \(\mathbf{r}_{dvec}\in\mathbb{R}^{256}\) using Equation (1), where \(f(\cdot)\) denotes the output of the model and \(\mathbf{w}\) denotes the parameters of the d-vector extraction model.  
\[\mathbf{r}_{log\_mel} =\text{Log\_Mel\_Spec}(\mathbf{s})\] \[\mathbf{r}_{dvec} =\frac{f\Big{(}\mathbf{r}_{log\_mel};\mathbf{w}\Big{)}}{\Big{\|}f\Big{(} \mathbf{r}_{log\_mel};\mathbf{w}\Big{)}\Big{\|}_{2}} \tag{1}\]  
The lengths of the FFT window and the frame shift used in the d-vector extraction are the same as those in MFCCs' extraction. Therefore, the first dimension of \(\mathbf{r}_{mfc}\) and \(\mathbf{x}_{mag}\) is the same.
# Prediction of Cyanobacteria Using Decision Tree Algorithm and Sensor Monitoring Data
## 2 Research Method  
### Research Target Areas and Blue-Green Algae Outbreak Status  
The Nakdong River, which is the target water body of this study, consists of eight multifunctional veins, which are the main hydraulic structures of its mainstream. It is a watershed that has undergone environmental changes driven by the installation of the veins under the Four Major Rivers Restoration Project in 2012. Therefore, it requires urgent watershed management plans according to those environmental changes. Since completion of the Four Major Rivers Restoration Project, the water depth of the Nakdong River has increased and the flow rate has decreased, both characteristics of which are indicative of a stagnating water body system. Therefore, the water environment is judged to have become lake-like in its properties. In recent years, algae warnings have been issued every summer owing to global heat wave and drought events. On 22 August 2018, the number of harmful blue-green algae cells in the Hapcheon Changyeong weir reached 1,264,052 cells per milliliter of water, which was considered the worst algae outbreak in the history of all 16 weirs since the initiation of the Four Major Rivers Restoration Project. Eight multifunctional veins have been installed in the mainstream of the Nakdong River as part of this restoration project: Sangju weir, Nakdan weir, Gumi weir, Chilgok weir, Gangjeong Goryeong weir, Dalseong weir, Hapcheon Changyeong weir, and Changyveong Haman weir, in order from the upstream to downstream reaches (Figure 1). The hydraulic/hydrological characteristics of the eight multifunctional veins are summarized (Table 1). Ever since the construction of the veins, the hydrological residence time has increased and the river system has displayed signs of stagnancy, with problematic algal blooms forming as a result of heat waves and drought.  
The water quality and aquatic ecosystems of the Nakdong River are deteriorating owing to an increase in the quantity, frequency, and duration of algal blooms in response to environmental changes (e.g., stagnation, higher water temperature, and increased pollution) (Figure 2). Direct countermeasures for these climate-mediated environmental changes are needed, in addition to policy management measures for integrated water management. Therefore, local authorities need to prepare realistic and feasible water management measures based on the conditions of the Nakdong River system.