esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Security Analysis  
Generally, random number generators are a great help in key management of cryptosystems. Forward secrecy and backward secrecy are two important features that the random number generators should have to be used for key processing. Forward security ensures that any attacker who knows a sequential subset of old bit patterns is unable to derive the subsequent bit patterns. Backward security is defined with the assurance that an attacker with the knowledge of contiguous subset of bit patterns is unable to trace back the preceding bit patterns.  
Let us assume two contiguous bit patterns as \(B_{1}\) and \(B_{2}\) each of n number of bits. As per the propagation criteria, at least \(\frac{n}{2}\) bits are changed from an initial bit pattern \(B_{0}\) to \(B_{1}\) and \(\frac{n}{2}\) bits from \(B_{1}\) to \(B_{2}\). With the increasing number of iterations, the change of bits also increases. The maximum probability that a bit pattern of n bits can be derived from \(B_{1}\) and \(B_{2}\) is calculated as:  
\(P([B_{1}:B_{2}]\to B_{3})\)\(=\frac{(n-r)!}{r!(n/2)!}\), where \(r\) is the difference of bits in two contiguous bit patterns. For large number of bits and more differences in bit patterns \(P([B_{1}:B_{2}]\to B_{3})\to 0\). It signifies that SRFG is able to provide forward security.  
Similarly, if an attacker knows bit pattern \(B_{N}\) and \(B_{N+1}\) and wants to trace \(B_{N-i}\) bit pattern with a random bit difference of \(r\), the probability that the bit pattern is revealed is:  
\[P(B_{N-i}\leftarrow[B_{N},B_{N+1}])=\frac{2^{n}}{n!.ir.(n-r)!}. \tag{26}\]  
Here, we assume that the attacker tries with \(i_{i}\)_th_ sequence assuming that each sequence has bit difference \(r\). Therefore, overall, bit difference becomes \(ir\). With large number of bit differences between two contiguous bit patterns, the probability of discovering any previous bit pattern eventually becomes 0 and thus, SRFG preserves backward security.  
For any random number generator, it is very important in cryptography to be immune against any distinguishing attack. Such an attack cryptanalyses on data encrypted or output by the system and distinguish it from random data. The correlation immunity and  
Figure 6: Distribution of \(p\)-values.  
algebraic immunity provides strength to SRFG on this point. The statistical validation also supports SRFG's indistinguishable property irrespective of inputs. For example: let us consider, \(E\) is an encrypted output from SRFG and \(S\) is a random bit pattern both of \(n\) bits. By the immunity property, SRFG develops a difference of difference of \(\frac{n}{2}\) bits propagated from initial difference. Therefore, the probability of distinguishing attack is:  
\[P(D)=\frac{2^{(n/2)}}{n!}\to 0\ \ \ with\ large\ n \tag{27}\]  
Therefore, SRFG is able to provide security parameters significantly and robust against cryptanalysis attacks; thus, efficient for cryptographic operations.
## 6 Irrigation Water Productivity  
This paper has largely been written from an engineering perspective, which predominantly defines WUE as the ratio of the irrigation water beneficially used by the plant or pasture to the water supplied through irrigation. This section will briefly discuss irrigation water productivity, with a focus on plant genetics and agronomic practices used to achieve higher yields using less water.  
Through plant breeding, scientists have managed to develop high-yielding crop varieties. This implies that, all other factors kept constant, with the same amount available water, farmers can achieve a higher irrigation water productivity. Detailed information on how the plant WUE can be improved through molecular genetics is described in [PERSON] et al. [38]. The research provides an overview of the manipulation of genes that strongly impacts on WUE such as these that control root traits and stomatal development. Some genetically modified varieties are also resistant to pests and diseases, leading to higher yields. A study conducted within the cotton industry in Australia found that the water use productivity had increased by 40% over a period of ten years as a result of yield increases achieved by developments in plant breeding, the use of genetically modified varieties, and improved crop and water management systems [5].  
Deficit irrigation, which is the application of less water than that is required by the plant or pasture, is a strategy that is often used when water is limiting. In a trial conducted in a dairy region of Victoria, Australia, where pasture is often irrigated, [PERSON] et al. (2016) demonstrated that lucerne under deficit irrigation can fully recover once full irrigation is restored, and thus ideal forage can be grown under water limiting conditions. [PERSON] et al. (2017) in a trial undertaken in a citrus orchard in Spain concluded that deficit irrigation strategies have the potential to improve WUE. [PERSON] (2017) proposed the adoption of deficit irrigation strategies in areas of China where conventional irrigation is no longer sustainable because of water shortages.
## 4 Discussion
### Telemedicine Technologies  
Various technologies are involved in the provision of telemedicine services [63; 89]. There is a clinical feedback loop between the physician and the patient, which determines the fulfillment of the actual condition of clinical telemedicine.  
Telemedicine programs are inherently complex compared to their traditional on-site healthcare counterparts. Despite the necessity of the pandemic, developing sustainable multispecialty telemedicine programs is difficult. Single-service programs, such as teleradiology programs, are standard. Providing integrated telemedicine systems requires interrelated interoperability of telemedicine solutions with adequately efficient infrastructure. Several factors are barriers to developing integrated telemedicine programs [89].  
Technological aspects that should be considered are tools, web services and ongoing service support. Currently, telehealth services enable 24/7 access to healthcare professionals through teleconsultations and elements of telecare and telemonitoring. Information and Communication Technologies (ICT) have great potential to support cost-effective and high-quality healthcare services. TM uses ICT to overcome geographical barriers, increase access to health services and increase the safety of patients and medical personnel in the face of the risk of microbial transmission and exceptional virulence, as in the case of the SARS-CoV-2 virus. Organizational issues of telemedicine also help with other tasks in the infrastructure of the healthcare provider, enabling: electronic appointments, virtual visits with a healthcare professional, access to health references via the Internet and using smartphone apps to track and monitor health statistics.  
Telemedicine solutions also depend on whether a sufficiently efficient infrastructure is available for its needs. Telemedicine is becoming one of the most important ways to provide care today and in the future. Adapting to telemedicine solutions and constantly training physicians to use new telemedicine technologies for further development is necessary. Telemedicine networks [6; 14; 90; 91] can provide services in the one-to-one (point-to-point), one-to-many (point-to-multipoint) and many-to-many (multipoint-to-multipoint) models. Initially, the telemedicine system was adopted to allow the patient to interact with a single physician, and the benefits cover many fields of teleradiology, teleneurology and others. Telemedicine communication models are depicted in Figure 4.
## 5 Performance Evaluation of MEC B5G Cellular Networks
### Proof-of-Concept Description
\begin{table}
\begin{tabular}{c c} \hline
**Hardware Name** & **Specifications** \\ \hline LTE RU & Frequency Band: Band 3 \\  & System bandwidth: 5 MHz \\ \hline Sub6 RU & Frequency Band: n 77 \\  & System bandwidth: 100 MHz \\ \hline mmWave RU & Frequency Band: n 257 \\  & System bandwidth: 400 MHz \\ \hline  & CPU: Qualcomm\(\oplus\) Snapdragon\({}^{\text{m}}\) 765G 5G mobile platform \\  & OS: Android \\  & Support Band: Band 3/n77/n257 \\  & Band 3 Tx Rate: UL \(<\) 18 Mbps, DL \(<\) 100 Mbps \\  & n77 Tx Rate: UL \(\leq\) 217 Mbps, DL \(\leq\) 2.13 Gbps \\  & n257 Tx Rate: UL \(\leq\) 273 Mbps, DL \(\leq\) 2.80 Gbps \\ \hline  & Model: dynabock G83/DN \\  & OS: Microsoft Windows 10 Pro \\  & CPU(Phy)/MEM: 4 Core/8 GB \\  & USB ports: 2 \\ \hline \end{tabular}
\end{table} 
Table 2: Hardware Equipment Condition. Figure 10: Outdoor PoC Field Design. where \(N_{\text{MEC}}\) denotes the number of MEC resources decided by the private (local) telecom operator's strategy, \(w_{k}\) (CPU cycles) represents the task converted from information \(b_{k}\) with task weight \(\delta\), computation resource is expressed as \(f_{k,j}\), and \(\varepsilon_{j}\) is the processing queue on the MEC side as seen in Equation (1). On the cloud side, the latency \(t_{k,\text{cl}}\) and cost \(c_{\text{cl}}\) are expressed as,  
\[t_{k,\text{cl}}=\frac{(1-\alpha_{k})b_{k}}{N_{\text{BH}}}+\frac{(1-\alpha_{k} )w_{k}}{N_{\text{cl}}f_{\text{cl}}}+\varepsilon_{\text{cl}} \tag{2}\]  
where \(N_{\text{BH}}\) denotes backhaul capacity, \(N_{\text{cl}}\) is the number of cloud resources, \(f_{\text{cl}}\) is computing resources, and \(\varepsilon_{\text{cl}}\) denotes the processing queue in the cloud, as seen in Equation (2). Because the measurement is performed using one sector, sector \(j\) is set to 1 and user \(k\) is one UE, because RU is able to allocate the maximum resource block (RB). It is possible to input highly reliable information as an input in the numerical calculation based on the measured delay performance.
# A Feedback Control Method to Maintain the Amplitude of the RF Signal Applied to Ion Traps
## 3 FPGA-Based Feedback Control  
### Previous Results  
It has been shown that the drift of the secular frequency of trapped ions can be reduced using a commercial universal PI controller (LB1005) [13]. The amplitude of the RF signal applied to the ion traps is obtained by mounting a capacitive voltage divider inside a helical resonator. The obtained RF signal was converted to a DC signal by a rectifier. The difference between the DC signal and set-point voltage was used as the error signal of the commercial universal PI controller. Then, the DC voltage, the output of the controller, and the RF signal from the signal generator were applied to the frequency mixer. The amplitude of the mixed signal was increased by the RF amplifier and helical resonator. Using this method, the drift of the secular frequency of the trapped ion was reduced from 0.25% to 0.05% [13].
## 3 Case Study (Levels and Options of Factors (Step 1 in Figure 3))
### Additional Factors for MASS (6-10 of Step 1 in Figure 3)
#### 3.3.3. Level of Data Transmission to RCC (LoDT) (Visibility on Screen and Sound at RCC)  
The speed of network connection between a ship and shore directly affects the level of data transfer and information that operators can receive at the RCC. The current satellite system commonly used for ships is L-band, and its bandwidth is low (e.g., 432 Kbps of the Inmarsat Fleet Broadband Service [36]). In this case, it is not easy to continuously and simultaneously send large amounts of data such as video data to shore. On the other hand, recent IT development has led to improved commercial satellite systems [37]. For example, StarLink plans to service 1 Gbps per user by utilising low earth orbit satellites [38]. In some sea areas close to land, ships can connect to 3G or LTE, whose maximum bandwidths are, e.g., from 2-3 Mbp to 40-50 Mbp of upstream speed in Japan, from a base station on the land. [PERSON] et al. [39] suggest that around 4 Mbps should be required for a sufficient remote control system.  
Nevertheless, the necessary bandwidth depends on the required resolution and level of redundancy, etc. Taking these into account, this study defines three levels of information level between a bridge and RCC, shown in Table 5. Level 2 utilises a similar situation to the demonstration project on remote control navigation that was carried out by using \"Shioji-maru\" of the Tokyo University of Marine Science and Technology in 2018.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Option** & **MO-1** & **MO-2** & **MO-3** & **MO-4** & **MO-5** & **MO-6** \\ \cline{2-7}  & **LoRC-1** & **LoRC-2** & **LoRC-1** & **LoRC-3** & **LoRC-3** & **LoRC-3** \\ \hline
**Bridge of a ship** & RO + SO & RO & RO & - & SO & - \\ \hline
**Remote control centre (RCC)** & - & SO & - & RO + SO & RO & RO \\ \hline \hline \end{tabular}
\end{table}
Table 4: Manning options in the case study.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **LoDT-1:** Static image with one picture/10 s \\  & - Identify vessel of a length of 45 m in 1 mile under the clear weather condition in the daytime. **LoDT-2:** (rough) Video \\ Visibility & - Identify vessel of a length of 45 m in 1 mile under the clear weather condition in the daytime. **LoDT-3:** (clear) Video \\  & - Identify vessel of a length of 45 m in 6 miles under the clear weather condition in the daytime.
## 5 HAWT Blade Design  
A focus is now being made on the HAWT due to its dominance in the wind turbine industry. HAWT are very sensitive to changes in blade profile and design. This section briefly discusses the major parameters that influence the performance of HAWT blades.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ref No.** & **Design** & **Orientation** & **Use** & **Propulsion** & *** Peak Efficiency** & **Diagram** \\ \hline
**1** & **Savonius** & **VAWT** & **Historic Persian** & **Drag** & **16\%** & \\  & **rotor** & & **windmill to** & & & \\  & & & **modern day** & & & \\  & & & **ventilation** & & & \\
**2** & **Cup** & **VAWT** & **Modern day cup** & **Drag** & **8\%** & \\  & & & **anemometer** & & & \\
**3** & **American** & **HAWT** & **18 th century to** & **Lift** & **31\%** & \\  & **farm** & & **present day, farm** & & & \\  & **windmill** & & **use for Pumping** & & & \\  & & & **water, grinding** & & & \\  & & & **wheat, generating** & & & \\  & & & **electricity** & & & \\
**4** & **Dutch** & **HAWT** & **16 th Century, used** & **Lift** & **27\%** & \\  & **Windmill** & & & **for grinding** & & & \\  & & & **wheat.** & & & \\
**5** & **Darrieus** & **VAWT** & **20 th century,** & **Lift** & **40\%** & \\  & **Rotor** & & **electricity** & & & \\  & **(egg beater)** & & & & & \\
**6** & **Modern** & **HAWT** & **20 th century,** & **Lift** & **Blade** & **efficiency** \\  & **Wind** & & **electricity** & & **Qty** & \\  & **Turbine** & & **generation** & & **1** & **43\%** & \\  & & & & **2** & **47\%** & \\  & & & & **3** & **50\%** & \\ \hline \end{tabular}
* Peak efficiency is dependent upon design, values quoted are maximum efficiencies of designs in operation to date [1].  
\end{table}
Table 2: Modern and historical rotor designs.
## 4 Model Validation
### Methods  
The model validation for this research was conducted in two sequential phases. First, the experimental analog of a pallet bending while in a warehouse rack support across its width was validated. This initial step allowed the researchers to confirm the accuracy of the model in replicating the pallet analog bending, before including additional complexities from the payload's characteristics. Second, the complete unit load segment was tested, validating the model's ability to replicate the pallet analog's deflection response while under the different payload's being studied.
# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
## 2 Background
### Reliability Block Diagram  
Reliability Block Diagram (RBD) [13] is widely used to model and evaluate the reliability of systems [8]. RBD allows both representing and estimating the reliability of a system through combining a collection of blocks, where each block expresses the reliability of a given element of the system, e.g., the reliability of a switch.  
As illustrated in Figure 2, an RBD has an input (_Source_) and an output (_Target_) that model the beginning and the ending of the system, respectively. Blocks are organised in series, parallel or their combinations. Furthermore, they have only two states, namely _failure_ or _working_. Failures are independent of each other, and each block has a reliability associated. For example, block \(B_{i}\) has reliability \(R_{i}(t)\) associated to \"working state\" at time \(t\). Meanwhile, \(1-R_{i}(t)\) represents the \"failure state\" at time \(t\).  
In the case a system works properly when its components are operational, the corresponding RBD is structured as a series of system's components. By considering a given series structure composed of \(n\) independent components, as presented in Figure 2a, the system reliability is calculated using Equation (1).  
\[R(t)=\prod_{i=1}^{n}R_{i}(t) \tag{1}\]  
\(R_{i}(t)\) is the reliability of block \(b_{i}\) at time \(t\). Consequently, the reliability at time \(t\) of a system having \(n\) components in series is calculated by the product of the reliability of the blocks that compose it. For instance, an RBD having three blocks has reliability equal to 0.729, if the reliability of each one is 0.9.  
Figure 1: (a) place, (b) arc and (c) transition and (d) place with token representations in CPN Tools.  
On the other hand, when a given system, made up of \(n\) components, only works if at least one of them operates correctly, the corresponding RBD is structured in parallel. In this way, the reliability at time \(t\) of the system shown in Figure 2b is computed by Equation (2).  
\[R(t)=1-\prod_{i=1}^{n}(1-R_{i}(t)) \tag{2}\]  
In this case, given a system having three blocks (each one having its reliability set to 0.9), its reliability is 0.999. It is worth observing how the organisation of the blocks affects the reliability of the entire system.  
In addition to series and parallel combinations, additional combinations can be created, such as series-parallel, parallel-series, bridge and _k-out-of-n_. The reliability of these additional combinations also use Equations (1) and (2). Figure 2c shows a RBD series-parallel where _Block01_ and _Block02_ are in parallel, and _Block03_ is in series. In this case, the reliability of parallel structure is calculated first, and then the series one. Assuming that each block has reliability equal to 0.9, the reliability of the whole system is 0.891.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 7 Evaluation
### Implementation  
ErrorExplainer is implemented in Python. Static analysis is built on top of Slither [23], which provides an analysis framework at the level of an abstract syntax tree (AST) of Solidity contracts. We first recover the inter-component structure of smart contracts, including inheritance hierarchies, modifiers, interfaces, and libraries, with the help of Slither. Within each contract, we then enumerate all transaction-reverting statements (require, assert, revert, throw) and assemble precise error contexts with the structural metadata. For ABI encoding and decoding of calldata, we use the web3.py library [24].
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 3 Proposed Method
### Deep Clustering Network
#### 3.2.1 User Multi-Source Behavior Sequence Feature Representation  
For the learning of user multi-source behavior sequence feature representation, the learning objective is an encoder-decoder model. The encoder consists of embedding layers and recurrent neural network layers. The embedding layers learn the feature representation of entities in user behavior events, which are then concatenated to form the feature representations of entities to form the feature representation of behavior events. The feature representations of consecutive user multi-source behavior events are input into the recurrent neural network, which outputs a fixed-length hidden state vector. A multi-output prediction decoder is constructed based on this vector. The deviation between the predicted values and the actual values is used as the objective function to adjust the network parameters. After training, the hidden state vector output by the encoder serves as the feature representation of the user multi-source behavior sequence. The structure of the encoder-decoder model is shown in Figure 2.  
For a multi-source behavior sequence s containing \(\ell\) consecutive behavior events, \(s=\left\{e_{i}\right\}_{i=1}^{l}\), where each behavior event \(e_{i}=\left(x_{1},\cdots,x_{m}\right)\) consists of \(m\) different entities. We utilize an embedding layer to learn the embedding vectors of the entities based on their correlation. For any entity \(x_{p}\in e_{i},1\leq p\leq m\), the embedding layer outputs the embedding vector of the entity as \(v_{x_{p}}=x_{p}\times V\), where \(V\) represents the embedding feature space of the entities. The higher the correlation between entities, the closer the distance between embedding vectors of the entities in the embedding space.  
Figure 1: User multi-source behavior deep clustering network framework. (The black symbols in the clustering represent different behavior patterns, and the red circles indicate the abnormal behaviors.)  
\(g\) predicted by the decoder. The deviation between the predicted and true parameter values and the true function parameter values is used as the loss function to adjust the model parameters. Once the model training is complete, we discard the decoder and extract the encoder. The hidden state vector of the user multi-source behavior sequence, which is outputted by the encoder, serves as the initial values for the features of the user multi-source behavior sequence. Using these initial feature vectors, we apply the k-means clustering algorithm to perform clustering and obtain k initial cluster centers \(\left\{u_{j}\in Z\right\}_{j=1}^{k}\).
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 4 Results  
Following the collection and preparation of data, as well as the development of the approach, the next stage is to evaluate how efficient and suitable our model is for predicting Microsoft stock price. Employing captions we present F-score and AUC values to assess our model's ability to forecast stock prices.
## 4 Results and Discussion
Nevertheless, it should be emphasised that during these events, the vertical position errors did not exceed the limit values for APV-1 operations. The largest maximum errors were observed at the KIRU station and they amounted to 23.41, 11.99, 9.72, and 15.63 m for the off, Klob, NeQuck, and EGNOS modes respectively. In summary, it can be concluded that the EGNOS model against the other three solutions (off, Klob, NeQuick) is definitely the best considering the accuracy of vertical positioning, reaching an unprecedented average value of 0.93 m. The differences between the solutions using the Klob and NeQuick models are similar (approximately 1.50 m), but they were clearly worse than the EGNOS model. Definitely the poorest results were achieved for the off solution, in which the average error of 3.47 m was 2-3 times worse than the other results.