esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Based Distributed Patient-Centric Image Management System
## 3 System Components
### Ethereum Blockchain  
Ethereum [6; 26] was developed based on the Bitcoin system and incorporated a programmable smart contract (SC) platform. In other words, SC is a computer program that stores rules for negotiating the terms of a contract. Programs can autonomously verify and execute contract-related agreements, thereby, reducing the cost of constructing and managing a centralized database. SC employs the Ethereum virtual machine that allows users to run SC within the blockchain network. In general, the fee mechanism of the Ethereum system depends on the value of gas [6]. A certain amount of gas is required to execute a SC and perform a transaction. A digital currency can be used to purchase gas. The actual transaction cost is defined as follows:  
\[\text{Ether}=\text{gas used}\times\text{gas price}.\]  
The Ethereum platform consists of two types of accounts: external owned accounts (EOAs) controlled by private keys and contract ones controlled by the contract code. EOAs are used to execute a transaction sending ether or to trigger execution of SC. An Ethereum transaction includes parameters such as recipient address, gas price, gas limit, ether values, account nonce, sender signature, and endpoint of the medical image. The Ethereum blockchain has an associated state database based on a Merkle-Patricia tree structure similar to IPFS objects. Therefore, we can model a blockchain using IPFS for more secure off-chain and on-chain storage of medical images. In the proposed scheme, we implemented the PCAC-SC protocol using an Ethereum blockchain to enable transparent controlled access, so that malicious entities could not access the medical images without patient authorization.
## 4 Experimental Results and Analysis
### Apache Kafka, Results and Analysis  
Apache Kafka is expected to be much faster than Quorum and Tendermint and not much faster than Hyperledger Fabric, since it uses Apache Kafka within Hyperledger Fabric. For Quorum and Tendermint this proves to be the case, Apache Kafka is much faster. However, Hyperledger Fabric is not as fast as we hoped. The results, shown in Figure 5, show a significant difference. At peak, for the 2 KB test, Apache Kafka achieves 241,124 tps, and on average around 107,187 tps. The spikes seen in Figure 5 are consistent with the phenomena observed by [PERSON] et al. [46] and is the result of re-balancing. The re-balancing feature used in Apache Kafka clients (and/or the Apache Kafka coordinator) allows the formation of a common group, and distributes a set of resources among the members of that group. Re-balancing occurs every time a member joins or leaves a group. In our case, since we cannot start every producer at the exact same time, a script starts sequentially, leaving the producers and consumers to gradually join and leave the group over the period of the test run resulting in re-balancing, and therefore a re-allocation of the resources occurs, hence the presence of spikes in performance.  
When comparing these results to other studies of Apache Kafka [46; 47; 48], we note that they deploy larger broker machines to manage more transactions. If we had deployed larger machines for the Apache Kafka test, system performance would be even better than we observed.
###### Abstract  
Reliable communication is the backbone of advanced metering infrastructure (AMI). Within the AMI, the neighbourhood area network (NAN) transports a multitude of traffic, each with unique requirements. In order to deliver an acceptable level of reliability and latency, the underlying network, such as the wireless mesh network (WMN), must provide or guarantee the quality-of-service (QoS) level required by the respective application traffic. Existing WMN routing protocols, such as optimised link state routing (OLSR), typically utilise a single metric and do not consider the requirements of individual traffic; hence, packets are delivered on a best-effort basis. This paper presents a QoS-aware WMN routing technique that employs multiple metrics in OLSR optimal path selection for AMI applications. The problems arising from this approach are non deterministic polynomial time (NP)-complete in nature, which were solved through the combined use of the analytical hierarchy process (AHP) algorithm and pruning techniques. For smart meters transmitting Internet Protocol (IP) packets of varying sizes at different intervals, the proposed technique considers the constraints of NAN and the applications' traffic characteristics. The technique was developed by combining multiple OLSR path selection metrics with the AHP algorithm in ns-2. Compared with the conventional link metric in OLSR, the results show improvements of about 23% and 45% in latency and Packet Delivery Ratio (PDR), respectively, in a 25-node grid NAN.  
Keywords:neighbour area network (NAN); smart grid (SG); optimised link state routing (OLSR) protocol; analytical hierarchy process (AHP); link metric; wireless mesh network (WMN) +
Footnote †: journal: _Energies_
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 5 Post-Processing and Parameters Extractions
### Path Loss, Received Power, and K-Factor  
The path loss (\(PL\)) can be calculated from the total sum of the squared amplitude of the measured multipath \(p_{l}^{2}\) and the antenna gains as [57]:  
\[PL(\mathrm{dB})=-10\log_{10}\left(\frac{\sum_{l=1}^{L}p_{l}^{2}}{G_{t}G_{r}}\right) \tag{3}\]  
where \(L\) is the total number of measured multipaths and \(G_{t}\) and \(G_{r}\) are the gains of the Tx and Rx antennas at the center frequency, respectively. Then, the received power can be calculated as:  
\[P_{r}(\mathrm{dBm})=P_{t}(\mathrm{dBm})-PL \tag{4}\]  
Figure 5: Power delay profile at a 5 m Tx-Rx separation distance in (**a**) the Manufacturing hall at Dokka Fastners AS, (**b**) the Test room at Dokka Fastners AS, and (**c**) NTNU MANULAB.  
Figure 6: Normalized power delay profile at a 5 m Tx-Rx separation distance in (**a**) the Manufacturing hall at Dokka Fastners AS, (**b**) the Test room at Dokka Fastners AS, and (**c**) NTNU MANULAB.  
where \(P_{t}\) is the transmitted power set at 0 dBm for all measurements. The K-factor is the ratio of the energy of the dominant path (\(E_{d}\)), which is the LoS path in our measurements, and the energy of the reflected and scattered paths (\(E_{Rs}\)) and can be defined as:  
\[K[\mathrm{dB}]=10\log_{10}(\frac{E_{d}}{E_{Rs}}) \tag{5}\]
## 4 Discussion
### Effect of Properties of ASW Particles  
The Small, Medium and Large ASW powders are different not only in particle size but also in chemical composition, as shown in Table 1. It is therefore difficult to separate the effects of particle size and composition. However, comparing the wear behaviours of the ASFs manufactured from the different powders both with and without heating can give good indications of the relative significance of particle size and composition.  
Comparing L-ASF and LH-ASF shows that heating the Large powder resulted in huge reductions in the COF from 0.48 to 0.06 and in specific wear from 0.02 to 0.008 mm\({}^{3}\) m\({}^{-1}\). Table 1 shows that there is a significant change in the chemical composition of Large ASW powder. The heated Large sample has a higher oxygen content than the non-heated Large sample. We also observed a colour change and a decrease in strength and hardness after heating, attributable to the oxidation of residual metal and weight loss. In other words, for the same particle size, a change in composition can result in a very different wear behaviour, indicating the importance of the mechanical properties of the ASW particles. The decrease in strength upon the heating of the ASW particles leads to a decrease in the frictional force required to cause surface deformation and therefore a low COF of the LH-ASF sample. The micrographs of the worn surfaces of the LH-ASF sample clearly show a change in wear behaviour, where pieces of the ASW particles end up becoming attached to the static partner, leading to abrasive wear and a resultant increase in the amount of wear.  
Heating the Small and Medium ASW particles results in little change in wear behaviour for the resultant ASFs, because of no significant compositional changes in these two powders. The micrographs of the worn surfaces of the SH-ASF and MH-ASF samples show very similar wear behaviour as opposed to S-ASF and M-ASF samples, with adhesive wear being dominant, abrasive wear observed to some extent and indications of oxidative wear and delamination as well.  
Ceramic particle size has no significant effect on the volume fraction of ceramic particles in the resultant MMSFs manufactured by infiltration casting. The effect of ASW particle size on the wear behaviour of the as-fabricated ASFs is therefore not expected to be large. However, particle size is still a factor which cannot be ignored. Table 2 shows that the COF and specific wear of the ASFs containing non-heated ASW powders increase with increasing ASW particle size, which can be partly attributable to the particle size effect. Different ASW particle size leads to different sized ligaments of the Al matrix network, which can affect the COF and wear of the ASFs.
# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 4 Experiment
### Data Introduction and Preprocessing
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Number of Samples** & **Total Duration (s)** \\ \hline RO-RO Ship & 5 & 1512 \\ Oceanliner & 7 & 938 \\ National Noise & 12 & 1134 \\ Motorobat & 13 & 1008 \\ Passenger Ship & 30 & 4256 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Information of radiated noise signal. Figure 16: Framing of the radiated noise signal. Figure 15: Radiated noise data of the passenger ship in the first 20 s.  
Figure 18 shows the Time-Frequency and Delay-Doppler features of a passenger ship. Figure 18a shows the Delay-Doppler feature of the ship and Figure 18b shows the Time-Frequency feature of the ship, with the signal energy mainly concentrated in the low-frequency band. From the analysis of the Figure 18a, the target has a relatively obvious Doppler frequency shift. In order to improve the accuracy of target recognition, all coordinate information is hidden from the input results.
## 3 Materials and Methods
* H1: There is a statistically significant difference in the level of knowledge sharing in Slovak enterprises based on the geographical location of the organization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region (NUTS II)** & **Bratislava Region** & **Western Slovakia** & **Central Slovakia** & **Eastern Slovakia** \\ \hline
**Regions** & **BA** & **TT, TN, NR** & **BB, ZA** & **KE, PO** \\ \hline Number of enterprises in 2013 & 1074 & 895 & 639 & 603 \\ Number of enterprises in 2014 & 1098 & 904 & 644 & 612 \\ Number of enterprises in 2015 & 1105 & 916 & 651 & 613 \\ Number of enterprises in 2016 & 1114 & 923 & 649 & 621 \\ Number of enterprises in 2017 & 1123 & 926 & 654 & 623 \\ \hline \hline \end{tabular} Source: own elaboration based on the Statistical Office (SU) of the Slovak Republic (SR) [55]. Note: BA—Bratislava Region, TT—Tranva region, TN—Trencin Region, NR—Nitra Region, BB—Banská Bystrica Region, ZA—Zilina Region, KE—Kosice Region, PO—Preslov Region.  
\end{table} 
Table 1: Regional structure of enterprises with over 50 employees. NUTS—Nomenclature of Territorial Units for Statistics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region (NUTS II)** & **Bratislava Region** & **West Slovakia** & **Central Slovakia** & **East Slovakia** \\ \hline
**Regions** & **BA** & **TT, TN, NR** & **BB, ZA** & **KE, PO** \\ \hline Sufficient size of research sample & 88 & 87 & 84 & 83 \\ Real size of research sample & 151 & 132 & 126 & 123 \\ \hline \hline \end{tabular} Source: own elaboration.  
\end{table} 
Table 2: Determination of research sample for individual Slovak regions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sector** & \multicolumn{5}{c}{**Share of Companies as a \%**} \\ \cline{2-5}  & **2013** & **2014** & **2015** & **2016** & **2017** \\ \hline Manufacturing & 40.2 & 37.9 & 40.1 & 37.6 & 39.1 \\ Agriculture, forestry, and fishery & 8.2 & 9.0 & 7.3 & 10.1 & 8.0 \\ Power industry and water management & 4.4 & 3.4 & 4.1 & 2.8 & 4.2 \\ Services & 34.0 & 36.7 & 37.2 & 38.1 & 38.3 \\ Other & 13.2 & 14.0 & 11.3 & 11.4 & 10.4 \\ \hline \hline \end{tabular} Source: own research.  
\end{table} 
Table 3: Percentage share of companies operating in individual sectors. * H2: There is a significant difference in the use of knowledge databases based on the geographical operation of the organization.
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
## 6 Conclusions and Future Work  
This paper proposed Soft Logical Rules enhanced Embedding (SoLE), a novel paradigm of KG embedding, which was enhanced with a joint training algorithm that learned entity and relation embeddings using both soft rules and KG facts simultaneously, as well as forward chaining inference over logical rules to generate more valid groundings. Specifically, SoLE contained two stages: grounding generation and embedding learning. In the first stage, we extracted soft rules with certain confidences via modern rule mining system and then used the rule engine to perform forward chaining inference over these soft rules and KG facts to generate more groundings. At the stage of embedding learning, we devised a joint training algorithm to optimize over both KG facts and soft rules simultaneously where groundings of soft rules were modeled by t-norm fuzzy logics and soft rules were modeled by their corresponding groundings. The truth values calculated by the groundings were used to estimate soft rules, where the confidence of one soft rule was treated as the probability it held. SoLE then amounted to minimizing a global loss over both the loss function for KG facts and the L2 loss for soft rules. In this manner, the facts in groundings could capture the logical background knowledge in these rules so as to learn better embeddings. To sum up, this paper devised a novel joint training algorithm that directly injected the background knowledge of soft rules into embeddings and introduced forward chaining to KG embedding model with logical rules. Experimental results on benchmark KGs showed that our method achieved consistent improvements over the state-of-the-art baselines.  
This research brought up some questions in need of further investigation. Firstly, our method could only support the Horn clause rules, which exclude negative atoms. Some other types of logical rules, such as \(\forall x,y,z:(x,\text{child\_of},y)\land\
eg(y,\text{nationality},z) \Rightarrow\
eg(x,\text{nationality},z)\), cannot be extracted and performed by our method since KG does not contain negative triples. Secondly, our method may not be suitable for the extremely large scale KG with a large amount of relations and facts. Given this kind of KG, massive rules would be extracted, and the cost for performing forward chaining would be intolerant due to the high degree of space and time complexity. For future work, we would like to investigate the possibility of modeling all kinds of soft rules using only relation embeddings to avoid grounding, which might be space and time inefficient with regard to the extremely large scale KG.  
Conceptualization, J.Z. and J.L.; methodology, J.Z.; software, J.Z.; writing, J.Z. and J.L.  
This research received no external funding.  
We would like to thank Network and Information Center, USTCfor providing the GPU servers to run our experiments.  
The authors declare no conflict of interest.
# Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data
## 3 Methodology
### Research Data  
The focal point of this study rests upon expressways, and for the purpose of calculating micro-segment speeds, DTG data sourced from freight vehicles and expressway node link data were employed. The study's aim revolves around partitioning lengthy road segments based on variations in vehicle speeds and subsequently generating speed data for each delineated segment. Consequently, emphasis was placed on expressways characterized by extended segment lengths and higher speed limits. This selection rationale stemmed from the prevalence of continuous entrance and exit ramp junctions, accident occurrences, and sporadic instances of congestion--features more pronounced on expressways as compared with those on urban roads. Urban roads, typified by relatively shorter segment lengths, lower speed limits, and fluctuations in traffic flow due to intermittent patterns, were thus excluded from the study's scope.  
To effectively capture prototypical vehicle driving behaviors and amass comprehensive position and speed data, DTG data from freight vehicles were harnessed. While DTG data can be culled from taxis, buses, and trucks, this study chose to focus solely on freight vehicles. The rationale behind this exclusion was rooted in the potential divergence in driving patterns exhibited by taxis and buses, largely stemming from their prevalence within urban domains. Notably, taxis and city buses often engage in frequent stops, dictated by factors like passenger embarkation, disembarkation, and service halts. Moreover, the presence of bus-exclusive lanes can introduce distinctive attributes to intercity buses' driving behaviors. In contrast, freight vehicles exhibit unique characteristics, predominantly traversing at lower speeds and frequently occupying the right lane. Aligning with the objective of securing congruence in driving patterns and data availability, this study concentrated on freight vehicle DTG data. A comprehensive overview of the obtained data's scope is enumerated in Table 3.  
Data extraction was undertaken based on expressway routes, utilizing the standard node link framework established by the National Transport Information Center [25]. The experimental setup encompassed 951 nodes and 2133 links situated on Expressway no. 50 within the Gyeonggi region. The attributes encompassed in this extraction process are itemized in Table 3.  
Table 4 further delineates a comparison between national node links for expressways and Gyeonggi-do regional node links applicable to urban freeways.  
Figure 6: Space segmentation according to the determination of UF. (**a**) N-th (**b**) N+1 st (**c**) N+2 nd spaces.
# Median Absolute Deviation for BGP Anomaly Detection
## 5 Conclusions and Future Work  
In this study, we have rigorously tested and proven the effectiveness and reliability of our anomaly detection method in the Border Gateway Protocol (BGP) environment. By applying our method to various cyber incidents, including internet worm attacks, unintentional configuration errors, and critical infrastructure failures, we have shown the versatility of our statistical approach.  
Moreover, evaluating MAD in comparison to deep learning techniques has demonstrated its competitive performance. However, as previously mentioned, our intention is not to compete with these deep learning techniques but to complement their detection methodology. For this reason, we acknowledge that a direct comparison among these different approaches might not fully highlight the unique benefits of our method, especially in scenarios demanding reduced computational complexity and enhanced adaptability.  
Key advantages of our approach include the following:  
Reduced Computational Demands: Our methodology excels in high-performance detection with significantly lower computational requirements, making it more accessible and adaptable across diverse network settings.  
Minimal Preprocessing Needed: Our method necessitates minimal preprocessing, which not only enables a faster anomaly detection process but also lowers the risk of introducing biases at the preprocessing stage.  
Suitability for Real-Time Applications: The reduced computational resources and streamlined preprocessing make our approach well suited for real-time applications. In BGP anomaly detection, the ability to quickly identify and address anomalies in real-time or near-real-time is crucial. It enables network administrators to proactively mitigate potential threats, preventing potential harm or disruptions.  
Detect zero day attacks: Our model can potentially detect unseen anomalies without requiring training on a specific pattern. This capability can be employed to refine and update the training datasets of machine learning and deep learning models, thereby by complementing these models with MAD, it can improve their ability to recognize and respond to new, unexpected anomaly types.  
While we recognize the strengths of both statistical and deep learning (DL)/machine learning (ML) models in anomaly detection, we support combining these methodologies to achieve a more accurate and effective detection framework. This balanced strategy takes advantage of both the quick adaptability and lower computational requirements of our method and the advanced pattern recognition capabilities of DL and ML models to provide a comprehensive solution to the ever-evolving challenges of cyber security.
## 6 Experiment and Result Analysis
### Evaluation Metrics and Experimental Parameter Settings
#### 6.2.2 Experimental Model Parameter Settings  
In accordance with the study requirements, the dataset undergoes initial preprocessing steps. Each frame image is cropped to a standardized size, followed by facial feature extraction using MTCCN. A diffusion model is then applied to denoise the images and eliminate artifacts. Subsequently, each video is processed through various input methods tailored to the respective networks for feature extraction, training, and predictive assessment of facial forgery. The dataset is partitioned into three segments, divided in a ratio of 60% for training, 20% for validation, and 20% for testing, to assess both its performance and generalization ability.  
Figure 14: Sample example of the FaceForensics++ dataset.  
In the inter-frame information extraction network of SEAR-Net, the dual-stream RNN branches \(g_{1}\) and \(g_{2}\) are each equipped with 32 GRU units. As a result, SENet attention is applied at the output layer of the RNN, with input and output dimensions of 2 and 64, respectively. Following this, an FC layer with input and output dimensions of 64 and 2, respectively, is connected at the output layer. Before entering the RNN network, a Dropout layer with a dropout rate of 0.3 is added to the input, and within the RNN network, four Dropout layers with a dropout rate of 0.5 each are inserted to enhance model generalization and prevent overfitting. For network training, the Adam optimization algorithm is chosen for parameter optimization, with a learning rate set to 0.002, and batch size and block size set to 2048 and 32, respectively. The epochs for \(g_{1}\) and \(g_{2}\) are set to 1000 and 800, respectively.  
In the intra-frame information extraction network of TE-WSDAN-DDNet, the WSDAN network, with an attention map threshold of 0.45, is integrated, and a Gram Block is introduced for deep texture feature extraction. The EfficientNetB4 model, pre-trained and used as the backbone network, is employed during training. The input image size for model training is set to 384 \(\times\) 384. Adam is selected as the optimizer, with cross-entropy loss serving as the loss function. The learning rate is set to 0.01, and ExponentialLR is utilized as the learning rate scheduler. The epochs and batch size for data training are 100 and 32, respectively. The GPU used for this experiment is the NVIDIA GeForce RTX4090, and the PyTorch 1.1.0 deep learning framework is employed. The primary evaluation metrics for analyzing model performance are AUC and ACC. Detailed parameters used in the experiments are listed in Tables 1 and 2.