esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Estimating Source Depth Based on the Adjacent Mode Group Acoustic Pressure Field
## 2 Theory
### Mode Extraction
According to the description of extraction conditions in singular value decomposition mode extraction [10], in order to fully sample the modes, all 48 hydrophones were taken as the acoustic pressure monitoring range. To ensure that the signal source passed through a sufficiently large range interval, all data during the time period that passed by the test vessel were classified as experimental data (48 \(\times\) 602,112). The mode extraction and singular value results obtained through singular value decomposition are shown in Figures 5 and 6. Figure 4: Mode distribution of the experimental environment. Figure 5: Mode extraction results of the experimental data. Figure 3: Waveguide sound speed profile of the experimental environment. The number of blue circles in the test data mode extraction results (Figure 5) was 48, which corresponded to the number of vertical array hydrophones, and the vertical coordinates corresponded to the depth of the 48 hydrophones. From the experimental data mode extraction of singular value results (Figure 6), the percentage differences between singular values were 10.71%, 11.35%, 12.5%, 9.50%, 18.55%, and 8.61%. Strictly speaking, 18.55% is not satisfactory. However, due to the error in the experiment, the rationality of some data could not be guaranteed. However, the rest of the data met the requirements for singular value results (5-15%). Generally speaking, the experiment was acceptable. Figure 7 illustrates the comparison of simulated waveguide modes and mode extraction results. The mode order obtained by comparing the mode extraction results with the simulation was 2, 4, 7, 5, 3, 5, 6, and the mode amplitude ratios were 0.104, 0.102, 0.067, 0.005, 0.046, 0.005, 0.048, respectively. The order of mode extraction was affected mainly by environmental biases, among which the vertical array in the experimental environment may have shifted, resulting in inconsistent data times. Additionally, the simulation environment was too ideal. So, there are differences between the theoretical result and the mode extraction results at the seventh mode when \(z\) is lower than 20.7 m in the third subplot in Figure 7. However, by comparing the mode distributions, the results of the mode extraction corresponded to the simulation results.
## 4 Results
### Routing Protocol-Based Simulation and Analysis  
The modified LEACH protocol used in WSNs to expand the network lifetime was considered for the power consumption analysis. In this protocol, the sensors arrange themselves in a cluster, and a single node performs as a cluster head. The cluster head finds the lowest degree of distance to the base station to decrease the power consumption in the cluster head nodes and the whole network. An improved LEACH protocol (I-LEACH) was implemented in communication and compared with the standard LEACH protocol. When the network parameters have been set, the simulation is run until all of the nodes drain their batteries for these two algorithms. Then, the network behaviour analysis is plotted, showing its lifetime and the amount of data sent. The proposed I-LEACH ensures that the elected cluster heads will be uniformly distributed over the network. Hence, there is no possibility that all cluster heads will be concentrated in one part of the network.  
The simulation results indicate that the proposed clustering approach is more energy-efficient, scalable, and effective in prolonging the network lifetime compared to the LEACH protocol. Figure 8a shows the simulation result of the lifetime of 100 sensor nodes (number of live sensor nodes vs. time), Figure 8b shows the simulation result of average energy consumption (energy vs. time), and Figure 9 shows the simulation result of the throughput between the LEACH and I-LEACH of 100 sensor nodes (number of packets vs. time). At the end of the simulation, the performance of the proposed I-LEACH protocol was evaluated. The data in Figures 8b and 9 prove that I-LEACH improves the energy consumption by around 43% and throughput by 40% in a 100-node network size.  
Figure 7: Distributed number of nodes, 20 and 80, in (**a,b**), respectively. Area length = 100 m, area width = 100 m, expected sensor reading = 100, and size of the data packet = five bytes.  
Application sensor nodes will periodically switch on their sensors and transmitters, sense the environment, and transmit data of interest at a constant periodic time interval. The I-LEACH protocol is highly recommended for networking sensor nodes in real-time deployments to increase the network's life expectancy and maintain throughput.
## 3 Results and Discussion
### Source Assessment
#### 3.5.3 Hierarchical Component Analysis  
Hierarchical cluster analysis (HCA) was utilized to examine the similarities and differences in PAH sources within agricultural soils across different land-use categories. Using [PERSON]'s clustering method and square Euclidean distance for comparisons, the results were depicted as dendrograms, displayed in Figure 6. A dendrogram, resembling a tree structure, illustrates the degree of similarity between parameters [43]. Ace, Acy, Flu, Ant, Phe, Flu, Pyr, Chry, BaA, BbF, BkF, BaP, DBaA, Ind, and BghiP are in one cluster, while Nap forms another cluster in research, coastal, rural, and local market areas. The first  
Figure 5: Principal component analysis (**(a)** research area, (b) industrial area, (c) local market area, (d) coastal area, (e) rural area).  
cluster likely originates from a combination of petrogenic and pyrogenic sources, while the second source is attributed to biomass burning [44], which is a major practice among the farming community in Bangladesh to reduce insect populations. On the other hand, in industrial areas, Nap, Ace, Acy, Flu, Ant, Phe, Flu, Pyr, Chry, BaA, BaP, DBaHA, Ind, and BghiP are in one cluster, while BbF and BkF form another cluster. The first cluster indicates the integration of pyrogenic and petrogenic sources. Furthermore, in the industrial area of Gazipur, frequent road construction by the city corporation and heavy traffic involved in transporting industrial products may contribute to the presence of BbF and BkF PAHs. These findings are consistent with those of [PERSON] et al. [40] and [PERSON] et al. [45], who reported that BbF and BkF PAHs originate from fossil fuel combustion.  
Figure 6: Hierarchical cluster analysis at (**a**) research area; (**b**) industrial area; (**c**) local market area; (**d**) coastal area; (**e**) rural area.
# TDM-PON PAM Downstream Transmission for 25 Gbit/s and Beyond
## 3 Simulation Results  
### Simulation Model Fine-Tuning  
In order to correctly simulate our experimental setup, we first measured a new BER curve for an equally spaced PAM4 generated with a data sequence of length \(2^{15}-1\) bits (PRBS15) on the existing experimental setup described in Section 2. The reduced PRBS length was imposed by memory constraints on our simulated PRBS sequences. Using equally spaced PAM formats allows a more straightforward and fair comparison between PAM4 and PAM8.  
The size of the masks is the major parameter that determines the optical budget in our model. The masks have fixed sizes, and according to the XG(S) PON standard [2], they are set similarly to the rectangular mask of the OLT's signal (see Figure 3a). In order to correctly calibrate its size for a \(3.8\times 10^{-3}\) target BER, its width matches the standard, i.e., 20% of the eye diagram width. The mask's height should be 50% of the eye's height to comply with the standard. However, since PAM4 includes three amplitude levels, a new mask has to be defined. We propose three identical masks with heights equal to 50% of the amplitude of each eye, which gives us around 16% of the total eye diagram's height, as shown in Figure 3b. In order to be closer to the experimental curves, the masks' height is set to 14% of the total eye diagram's amplitude in our simulations.  
The encoder noise setting that allows the best resemblance between experimental and simulation results was found to be 500 a.a.u, which represents a noise amplitude of \(\sim\)18 mVpp on each level for a 120 mVpp PAM4 signal. Figure 4 shows a quite good match between the simulated and the  
Figure 3: Mask of the eye diagram for OLT transmitter (**a**) and proposition for PAM (**b**).  
Figure 2: Gaussian noise representation on each PAM4 level for different standard deviations (blue: low standard deviation; green: medium; orange: high).  
experimental back-to-back BER curves for a transmitted PRBS15 sequence. The 12.5 Gbit/s NRZ curve shows the detection of the MSB in both experimentation and simulation.
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 1 Introduction  
With the emergence of the Internet of Things (IoT) and continuous development of communication techniques, increasingly more facilities are connected to the Internet. At the same time, a flood of information swarms into the fusion center (FC) waiting to be processed.  
Wireless sensor network (WSN) is a typical data-driven application in IoT, where multiple sensor nodes are deployed dispersedly aiming at observing the information source. The observed information is then transmitted through wireless channels, reaching the FC for further data gathering and recovery. Typical examples of WSN include forest salinity monitoring, smart parking, intelligent transportation safety supervising, and so on.  
In WSN, the freshness of the data is essential for taking prompt actions. For example, in a pedestrian detection system, timely data from sensors in-vehicle and infrastructure is critical to avoid collision and ensure the safety of pedestrians. In smart parking, the occupancy of the parking lot is vital for the users. In environment supervising, fresh data from the sensors is urgently wanted to better monitor the air pollution condition [1]. The above examples show the significance of the freshness of the received information in wireless sensor networks since it is transmitted to operate, supervise, and monitor the systems.  
Aside from the freshness, ensuring the fidelity, or accuracy of the received data is also of paramount importance. In wireless communication systems, diversity techniques are applied as useful tools for combating fading effects and improving the quality of the received signal [2, 3, 4, 5, 6]. The fundamental diversity combining is the selection combining (SC) scheme, where there is a signal to noise ratio (SNR)-based SC scheme [3] and log-likelihoodratio (LLR)-based SC scheme [5]. For the LLR-based SC scheme, it selects the branch based on the LLR. For SNR-based SC scheme, it selects the branch that provides the largest SNR from several diverse branches. This approach can effectively improve the fidelity of the ultimately received data.  
Based on the background information, we can conclude that when accounting for these two metrics, on the one hand, better freshness can be achieved by waiting for observations from fewer sensor nodes, yet it leads to a decline in the fidelity as there might be insufficient observations; On the other hand, a higher fidelity of transmitted data can be obtained when FC receives more observations from sensors. However, the data may become stale since there may be a long time for receiving enough observations. Motivated by the opposite trend of two metrics affected by the number of sensor nodes, we aim to find the optimal number of sensor nodes by carefully characterizing the data freshness and fidelity using the age of information (AoI) and minimum mean square error (MMSE) metric.
## 2 Background and Related Works
### Impact of Blockchain on Accounting
There is a recognised need for accounting professionals, especially forensic accountants, to adapt and develop technical skills suitable for detecting fraud within blockchain systems, as traditional methods may not suffice [26]. These forensic accountants, for instance, face the task of navigating through vast and complex databases to detect patterns of fraud. Blockchain's distributed data organisation can pose both opportunities and challenges in this regard [35]. Additionally, [16] notes the concerns surrounding the adaptation of existing legal frameworks, the technical complexity of blockchain systems, and the need for widespread understanding and trust in its mechanisms. These studies suggest that while blockchain can play a significant role in mitigating fraud, there is still much to be explored, particularly in terms of practical applications, regulatory responses, and the integration of this technology with existing financial systems [5]. Despite increasing research on blockchain applications in accounting, there is still a need for a complete framework that integrates blockchain technology with traditional accounting systems to enhance financial reporting and liquidity management [5]. Previous studies have primarily focused on the general benefits and challenges of blockchain in accounting, such as increased transparency, immutability, and energy efficiency. However, specific problems in existing accounting systems, such as privacy concerns, scalability issues, and regulatory compliance, have not been fully addressed. The BFS framework proposed in this paper aims to address this gap by using the unique features of two distinct blockchain architectures--Bitcoin and Corda--to create a hybrid solution for these specific challenges. The dual-blockchain approach of the BFS framework ensures privacy and confidentiality by incorporating Corda's privacy-oriented architecture, which allows sensitive financial information to be shared only among relevant parties. Additionally, the framework uses Corda's scalability features, such as its ability to handle complex transactions and support off-chain processing, to improve the efficiency of financial reporting and liquidity management processes. The BFS framework integrates smart contracts and automated compliance mechanisms to ensure that financial transactions adhere to regulatory requirements and accounting standards, reducing the risk of fraud and errors. By combining the strengths of both the Bitcoin and Corda architectures, the BFS framework offers a solution that enhances the overall performance and security of accounting systems, distinguishing it from existing approaches in the literature.
# Security Analysis of the Symmetric Cryptosystem Tiny Jambu
## 2 The TinyJambu Cryptosystem: An AEAD Scheme
### Operational Mode for TinyJambu  
In the way it operates, we might consider TinyJambu a sequential mode of encryption/decryption similar to a stream cipher mode because the sponge structure allows it. The whole operational mode is as follows:  
1. _Key Setup_: introduction of the key \(k\) by means of the permutation \(P_{1024}\). Set the 128-bit state \(S\) as 0. Update the state using \(P_{1024}\).
2. _Nonce setup_: introduction of the nonce \(N\) in three blocks of 32 bits with \(FrameBits=1\). **for**\(i\) from 0 to 2: \(S_{[36\dots 38]}=S_{[36\dots 38]}\oplus FrameBits_{[0\dots 2]}\) Update the state using \(P_{384}\) \(S_{[96\dots 127]}=S_{[96\dots 127]}\oplus N_{\{321\dots 321+31\}}\) endfor
3. _Processing the blocks of associated data_: introduction of the different 32-bit blocks of associated data (\(allel\) is the length in bits of \(AD\)). Here, \(FrameBits=3\). **for**\(I\) from 0 to \(\left\lfloor adlen/32\right\rfloor-1\): \(S_{[36\dots 38]}=S_{[36\dots 38]}\oplus FrameBits_{[0\dots 2]}\) Update the state using \(P_{384}\) \(S_{[96\dots 127]}=S_{[96\dots 127]}\oplus AD_{[321\dots 321+31]}\) endfor
4. _Encryption_: use \(FrameBits=5\), and the state is updated by means of the permutation \(P_{1024}\). Next, sequentially introduce 32-bit blocks \(M_{i}\) of plain text to obtain 32-bit blocks \(C_{i}\) of ciphered text (\(mlen\) is the length in bits of \(M\)) **for**\(i\) from 0 to \(\left\lfloor mlen/32\right\rfloor-1\): \(S_{[36\dots 38]}=S_{[36\dots 38]}\oplus FrameBits_{[0\dots 2]}\) Update the state using \(P_{1024}\) \(S_{[96\dots 127]}=S_{[96\dots 127]}\oplus M_{[32\dots 321+31]}\) \(C_{[321\dots 321+31]}=S_{[6\dots 95]}\oplus M_{[321\dots 321+31]}\)end for
5. _Finalization_: this phase carries out the generation of the _tag_ and uses _FrameBits_ = 7. Notice that two different permutations \(P_{1024}\) and \(P_{384}\) are employed depending on which bits of the _tag_ are being computed. \(S_{[36\dots 38]}=S_{[36\dots 38]}\oplus\textit{FrameBits}_{[0\dots 2]}\) Update the state using \(P_{1024}\) \(T_{[0\dots 31]}=S_{[64\dots 95]}\) \(S_{[36\dots 38]}=S_{[36\dots 38]}\oplus\textit{FrameBits}_{[0\dots 2]}\) Update the state using \(P_{384}\) \(T_{[32\dots 63]}=S_{[64\dots 95]}\)
6. _Operations in reception_: the receiver performs the following operations: _Decryption_: this is analogous to _Encryption_ but substitutes \(M_{[321\dots 321\!+\!31]}\) with \(C_{[321\dots 321\!+\!31]}\) in order to recover the original 32-bit blocks \(M_{i}\) of plain text (\(M\)). _Generation of \(T\)_: the receptor tag (\(T^{\prime}\)) is generated in the same way as the sender tag (\(T\)). Then, both tags are used for verification purposes. _Verification_: If \(T^{\prime}\) = \(T\), then accept the plain text (\(M\)); otherwise, reject it. After the description of the TinyJambu operational mode, we proceed with the security analysis of this cryptosystem.
## 3 Results  
In a previous study, we showed that _E. faecalis_ grew in biofilms on the inner walls of dental roots after 72 h of incubation and that BTA was reliable in evaluating the biofilm load before and after different treatments [6]. Here, the same experimental procedure to colonize the dental roots was used. As expected, dental roots were colonized, and a load equal to 6.00 \(\pm\) 0.98 log\({}_{10}\) CFUs/mm\({}^{2}\) was detected (Table 2). The mean bacterial load after treatment, mean survival rate, and \(p\) values are reported in Table 2. The greatest reduction of bacterial load was observed in group E with a survival rate of 23.16%. Group D came at the second position with a survival rate of 32.50%, followed by group A (positive control group) with a survival rate of 33.33%. In test groups, the least reduction of bacterial load was reported in group B, as the survival rate was 94.33% (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Study Groups**} & **Mean Bacterial** & **Mean** & _p Value*_ & _p Value_ & _p Value_ \\  & **Load** & **Survival** & **versus** & **versus** & **versus** \\  & **(log\({}_{10}\) CFUs \({}^{\dagger}\)/mm\({}^{2}\))** & **Rate** & **Control** & **Group** & **Group A** & **Group B** \\  & **(mean \(\pm\) SD \({}^{\ddagger}\))** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
**Control Group** & 6.00 \(\pm\) 0.98 & 100.00 & & & \\ \hline
**Group A** & 2.00 \(\pm\) 2.07 & 33.33 & \(<\)0.01 & & \\ \hline
**Group B** & 5.66 \(\pm\) 0.75 & 94.33 & 0.63 & \(<\)0.01 & \\ \hline
**Group C** & 5.23 \(\pm\) 1.23 & 87.16 & 0.35 & \(<\)0.01 & 0.65 \\ \hline
**Group D** & 1.95 \(\pm\) 1.51 & 32.50 & \(<\)0.01 & 0.89 & \\ \hline
**Group E** & 1.39 \(\pm\) 1.64 & 23.16 & \(<\)0.01 & 0.42 & 0.43 \\ \hline \hline \end{tabular}  
* CFUs: colony-forming units; \(\ddagger\) SD: standard deviation values;
* _p_\(\leq\) 0.05: statistically significant.  
\end{table}
Table 2: _Enterococcus faecalis_ CCM2541 counts in control and experimental groups.  
The reductions of bacterial load in groups of combined PDT with chemo-mechanical treatment (groups D and E), and group A (positive control group) were significant (\(p<0.01\)) when compared to that of the negative control group. No statistically significant difference was observed between the positive control group (group A) and groups of combined PDT with chemo-mechanical treatment (group D (\(p=0.89\)) and group E (\(p=0.42\))).  
The groups B and C (without NaCl irrigation) showed slight reductions of bacterial load without statistical significance (\(p>0.05\)) when compared to the negative control group. A significant reduction of bacterial load in group A (positive control group) was observed when compared with groups B and C (\(p<0.01\)).
# A Systematic Review of Cybersecurity Risks in Higher Education
## 9 Analysis and Discussion of Cyber Risks and Countermeasures in HE
### Integrity Loss in Key Assets  
One of the most critical information assets managed at the university is student records and the finishing diplomas. The incentive is obvious for students to hack and illegitimately change their grades for the better. However, a large-scale incident with changed exam grades and a loss of integrity in issued diplomas would be critical. Untrustworthy diplomas would be devastating for most HEI. Furthermore, integrity attacks on research data sets to sabotage competitors is also a likely scenario. Universities are also responsible for various payments and invoicing. Consider the possibility of such information being wrong, and either paying the wrong employee or billing the wrong recipient. Integrity risks are also present in HEI.  
_Countermeasures_: Identify the assets with mission-critical integrity and risk manage them appropriately. Implement proper infosec management, including access control, backup functionality, and integrity checks.
## 6 Methodology of Sample Implementation
### Implementation of VR Conferencing
#### 6.1.3 NetCode for Status Synchronization  
In online games, in order to allow multiple players to play games together on different computers, a mechanism is needed to ensure that all computers are synchronized such that players can accurately see the performance of each player smoothly; players' input should influence the game state. We use the NetCode [53] network architecture of Unity to synchronize the status information of roles in multiple terminals, mainly including location and orientation information. The network code has always been one of the most difficult parts in game development. The Unity NetCode software package provides a dedicated server model with client prediction, which can be used to simplify the development of multiplayer games. It is currently in the experimental phase. Listing 1 is some pseudocode for Netcode.  
```
1 Vector3 Position=newVector3;
2
3 Move()
4{
5 if(this.IsServer)
6{
7 newPosition=GetPosition();
8 Position=randomPosition;
9}
10 else
11{
12 SubmitPosition();
13}
14}
15[ServerRpc]
16 SubmitPosition()
17{
18 Position.Value=GetPosition();
19}
20
21 voidUpdate()
22{
23 transform.position=Position.Value;
24}
```  
**Listing 1** pseudocode of Netcode in unity