esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 7 Conclusions  
The communication technology is a vital component of an SPS system, and it is necessary to have an effective communication model that provides reliable and faster occupancy data collation and dissemination between different entities. This paper explored various aspects such as the position of the BR in a mesh network, the presence of a single data collator against multiple data collators, their relative positions with respect to BR, consumers and the effects of hybrid radio duty cycling for mesh devices. It also proposed a concise data format that accommodates a large number of occupancy data (up to 640 parking slots) in a single data packet. This reduces the number of data packets exchanged between the data collators and data consumers. Lowering the radio activity directly improves the energy efficiency of the system. Along with that, the concise data format presents a short http message and improves the web page load time. Five different communication models are evaluated for their efficiency in providing low latency and energy efficient communication. The best two models were further subjected to a scalability test in a larger 15 \(\times\) 15 grid network. A multiple data collator model where the data  
Figure 12: Occupancy data latency in a large network.  
Figure 13: Performance in a 10 \(\times\) 10 grid vs. 15 \(\times\) 15 grid (**a**) Time to reach local consumers. (**b**) Percentage of time when radio was active.  
collators are adjacent to the BR and are positioned at the center of the network is identified as the best model for providing efficient communication between data producers and consumers. Having multiple data collators adjacent to BR reduces congestion around the BR in a large network and improves their reliability. Their position at a center point reduces the hop distance between the nodes and reduces latency. Congestion avoidance and shorter communication paths present an energy-efficient system. Thus, the strategic positioning of multiple data collators reduces data transit time, offers a higher data reliability and lowers the power consumption of the mesh devices.  
Conceptualization, T.A.; methodology, T.A. and M.P.; software, T.A.; validation, T.A. and M.P.; formal analysis, M.P. and T.A.; investigation, T.A.; resources, M.P.; data curation, T.A.; writing--original draft preparation, T.A.; writing--review and editing, M.P. and T.A.; visualization, T.A.; supervision, M.P.; project administration, M.P.; funding acquisition, Not Applicable. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data are available with the corresponding author and can be provided on request.  
The authors declare no conflict of interest.
# Hardware Countermeasures Benchmarking against Fault Attacks
## 3 Dfa on AES
### Type of Faults  
The types of faults that are considered in the theoretical models of DFA that can be achieved experimentally are briefly described below and listed in Table 2. These faults have been classified according to the requirements of the theoretical models of the DFA. For example, it is necessary to differentiate between faults inserted in multiple bits because they can occur in the same byte in an odd or even number, or on the contrary, they can occur between different bytes in an odd or even number. In the case D, the injection of a single-byte is one that considers the change of a whole byte, not considering the reset of all its values. In case F, the faults considered are those that are randomly produced, i.e., those that can be of any type without any control over them or their repetitiveness.  
The type of fault denoted as A can be considered the most difficult to achieve due to the complexity of changing the value to a single bit of the cipher state, for example, between the operations during encryption process, at a given clock cycle and which must be repeated in different attacks for the same cipher input. On the contrary, the fault denoted as F can be considered the simplest to achieve, since, being a random fault, it can occur at any clock cycle and location.  
Thus, the attacker capable of obtaining faults of any type is considered the most powerful. On the other hand, the attacker who is only able to inject one type of fault or faults of type F will be the one who has less power when it comes to achieving the theoretical scenarios and obtaining information about the circuit, since his control over the generation of faults is minimal.  
When considering the fault injection into the AES cipher, depending on where the fault occurs (the state matrix, or on the S-box(), or KeySchedule() operation) the capacity of the attacker varies notably, since the successful insertion of faults will depend on the type of implementation of the AES cipher used. For example, in the case of an attack targeting the KeySchedule() generated and stored in advance, it will be difficult to inject faults inside it. On the other hand, on a rolled implementation of the AES cipher, performing a single round each clock cycle forces the attacker to optimize the fault injection in order to make them occur herein on specific clock cycle. Note that, in order to study and compare the total coverage of each of the countermeasures, we will consider that the attacker is capable of achieving any type of fault in any scenario.
## 2 Model and Methods
### Lyapunov Exponents
#### 2.2.1 Generic Attractor Characteristics  
To date, we have identified the following generic attractor characteristics that are predicted by LEs. A common attractor LE spectral boundary outlines the borders of each of the attractor and window domains. A 2-zero LEs' signature that is common to several other spectral patterns indicates the beginning and ending of each attractor's existence [37]. That is, as the injected signal is either increased or decreased, the point of the attractor's origin or demise is a pair of zero LEs. In between these two extremes, the attractor's spectral pattern evolves as the dynamics dictate. Figure 2a displays each attractor via its LE pattern in the context of the total unstable LIS domain for this set of parameters. In Figure 2b-d individual LE attractor domains are displayed corresponding to A, B, and C in Figure 2a. Each exhibits the characteristic boundary of converging and diverging Lyapunov spectra and the signature zero pair of LEs. The 8 known attractors are color coded as follows: AI is burnt orange, AII is red, AIII is green, AIV is purple, AV is blue, AVI is yellow, AVII is gray, and AVIII is pink.  
A cluster of asymmetric LE bubbles along with their signature zero-pair LEs' at their apexes represent a forward (in AVII and AVIII) or backward bifurcation sequence, (in AIV); how the sequencing evolves can be interesting. See results in Section 3.1. Figure 2a displays one symmetric bubble in AIII, but this characteristic pattern indicates a different type of dynamics other than the asymmetric bubbles. The following discussion elaborates on symmetric-like LE bubbles.
## 3 Integrity Monitoring Method of Space-Ground Integration
### Inter-satellite consistency check  
Compared to traditional SAIM techniques, one of the most important features of A-SAIM lies in the fact that A-SAIM will use inter-satellite links to realize the inter-satellite consistency check. This will greatly benefit the integrity monitoring capability for the ephemeris anomalies (specifically, orbit faults in the ephemeris) and the clock anomalies. First, this module detects the anomalies in the orbit parameters of the ephemeris by comparing the satellite-to-satellite distance measured by inter-satellite links and the distance derived from the ephemeris of these two satellites.  
Second, small faults in the onboard clock can be detected by comparing the local time and the joint atomic time. The local time is provided by the onboard atomic clock, and the joint atomic time is obtained by integrating the onboard atomic clock information from all the satellites and the information from the inter-satellite links. The joint atomic time is more stable than the local time and thus, it can be used as a reference to detect the small faults in the onboard clock of each satellite. If there is any anomaly detected in the two steps above, then the satellite will inform the users with the broadcast message or the non-standard codes.
## 2 Results and Discussion
### Characterization of Gelatin from the Bones of Skipjack Tuna (STB-G)
#### 2.1.2 Amino Acid Composition of STB-G  
The amino acid composition and MW distribution are two key factors that influence the properties of gelatin [20]. As shown in Table 1, gelatin (STB-G) and Type I collagen (STB-C) from the bones of skipjack tuna had similar amino acid compositions. Glycine was the most abundant amino acid of STB-G and STB-C with contents of 340.3 and 339.1 residues/1000 residues, respectively, which is because approximately 50-60% of \(\alpha\)-chains consist of typical tripeptide repetitions (Gly-X-Y), where X is mostly proline and Y is mainly hydroxyproline [21; 22]. In addition, STB-G and STB-C were rich in alanine, proline, and hydroxyproline, in decreasing order, which is consistent with previous reports that those are the main amino acids in gelatins [20; 23; 24].  
Hydroxylproline can form hydrogen bonds through its hydroxyl group to stabilize the triple-stranded collagen helix [22; 25]. Pyrrolidine rings of imino acids (proline and hydroxyproline) are also confirmed to restrict the changes of the secondary structure of the polypeptide chain, and hence  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Amino Acid** & **STB-G** & **STB-C** \\ \hline Hydroxyproline (Hyp) & 72.5 & 73.8 \\ Glutamic acid (Glu) & 64.7 & 66.7 \\ Aspartic acid (Asp) & 46.1 & 46.8 \\ Threonine (Thr) & 25.6 & 25.2 \\ Serine (Ser) & 36.4 & 33.3 \\ Proline (Pro) & 104.8 & 104.4 \\ Glycine (Gly) & 340.3 & 339.1 \\ Alanine (Ala) & 125.3 & 126.3 \\ Cysteine (Cys) & ND & ND \\ Valine (Val) & 25.4 & 26.0 \\ Methionine (Met) & 14.7 & 14.5 \\ Isoleucine (Ile) & 11.4 & 12.7 \\ Leucine (Leu) & 25.2 & 26.0 \\ Tyrosine (Tyr) & 4.3 & 2.9 \\ Phenylalanine (Phe) & 13.8 & 14.3 \\ Hydroxylysine (Hyl) & 5.6 & 4.9 \\ Lysine (Lys) & 29.2 & 29.5 \\ Histidine (His) & 5.5 & 5.3 \\ Arginine (Arg) & 49.2 & 48.3 \\ Total & 1000.0 & 1000.0 \\ Imino acid (Pro + Hyp) & 177.3 & 178.2 \\ \hline \hline \end{tabular} ND = not detected.  
\end{table}
Table 1: Amino acid composition of gelatin (STB-G) and Type I collagen (STB-C) from the bones of skipjack tuna (residues/1000 residues).  
assist in reinforcing the thermal stability of the triple helical structure [14; 26]. Therefore, the amount of imino acids highly affects the stability of the triple helix structure of the reatured gelatins [5; 22]. As shown in Table 1, the content of imino acids of STB-G was 177.3 residues/1000 residues, which is similar to those of gelatins from the skin of dover sole (173-183 residues/1000 residues) [27] and bigeye snapper (186-187 residues/1000 residues) [28], but significantly lower than that of bovine gelatin (219.0 residues/1000 residues) [8]. Therefore, the helices of STB-G might be more unstable than those of bovine gelatin due to their lower contents of imino acids.
## 2 Measurement Setup  
### Measuring Stations  
Three measuring stations were designed to test the adaptive data transmission algorithm developed for a system of inertial sensors. Figure 1 presents the structure of two measuring stations. The first station is marked with the Roman number I. It contains the WSS (Wearable Sensor System) described in detail in Section 2.2, router (RT) with an access point (RT-N10U) and PC server. The WSS radio transmits data from sensors in the Wi-Fi 2.4 GHz standard. The frames transmitted by the WSS were filled with data from the sensors, and the value of the transmission filter control parameter was simulated, which is described in Section 3. In turn, the RT receives Wi-Fi frames containing data transmitted by the WSS, and then the RT via a wire (cable with RJ45 plug) sends Ethernet frames using UDP (User Datagram Protocol) to the PC server, where the data are archived.  
The station marked with II (Figure 1) was designed to test the network load in the context of radio band occupancy. Station II is an extended version of the previously described Station I. Station II additionally includes SA--(spectrum analyser RSA5065-TG), which allows for density spectrogram registration. The AARONIA HyperLOG 7060 antenna was attached to RSA5065-TG.  
Figure 2 presents the station for the analysis of network traffic generated by the WSS. Like in the case of the stations I and II, the RT allows for the registration of only data frames (Ethernet), whereas the network traffic is monitored in a wider range via the sniffer-ALFA AWUS036 ACH AC1200. The sniffer connected to the PC server via the USB port was used  
Figure 1: Station for measuring the network band occupancy.  
to receive Wi-Fi frames. The measuring station makes it possible to monitor the simulated load on the radio network with a different amount of transmitted data, which is described in more detail in Section 4.  
In the stations presented in Figures 1 and 2, the network created by the WSS, the RT with an access point and the PC server use the IPv4 protocol. The Beacon time (packets sent by the access point to synchronize a wireless network) of the RT was 100 ms, the DTIM (Delivery Traffic Indication Message) value was 3. The Wi-Fi access point channel was selected automatically. The data from the WSS module are sent to the PC server using the UDP. The data section of the UDP frame in the context of the adaptive transmission algorithm is discussed in more detail later in the article.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 5 Clinical Applications of Image Fusion in Liver Cancer Diagnosis and Treatment
### Ultrasonography/MRI, Ultrasonography/CT and Ultrasonography/PET-CT
#### Utility of CEUS/MRI in Radiofrequency Ablation of Liver Cancer  
Radiofrequency ablation of liver has been widely applied in the radical treatment of small liver and multinodular liver cancers due to multiple advantages of safety, simplicity and minimal invasion. The 5-year survival rate following radiofrequency ablation of small liver cancer is reported to be close to that of surgical resection [173]. Based on the advantages of CEUS/CT fusion imaging technology, local ablation of liver tumors has gradually become an important supplementary adjunct to the surgical treatment of primary liver cancer and colorectal cancer with liver metastases.  
CEUS/CT fusion imaging has many advantages over conventional ultrasound-guided treatment for liver lesions that are difficult to ablate. Conventional Ultrasonography is unsuitable in the following situations: (1) the target lesion for ablation shows a similar echo signal to surrounding sclerosing nodules of liver tissue, which are difficult to identify owing to low spatial resolution, (2) some target lesions cannot be clearly displayed due to the interference of diaphragm movement or close proximity to the diaphragm, colon and gas movement, and (3) after repeated TACE or RFA treatment, the local echo of advanced liver tumor lesions is mixed, the lesion boundary is blurred, and it is difficult to identify the initial and recurrent lesions. CEUS/CT fusion imaging technology solves the above issues by not only taking into account the advantages of the real-time and continuous dynamic observation of the ablation effect, but also combining the characteristics of high-resolution-enhanced CT localization of lesions. Therefore, accurate needle placement for lesions is achieved under difficult conditions, and the flexibility of Ultrasonography is used to adjust the direction of intraoperative needle insertion to pinpoint the locations of difficult lesions. Recent reports indicate that the success rate of fusion imaging-guided radiofrequency ablation technology has increased to 93% [8]. CEUS/CT fusion imaging additionally allows multiple ablation plans for complex liver lesions based on the characteristics of enhanced CT three-dimensional imaging, the reasonable placement of needles and the seedling path, along with the formulation of other specific plans to reduce the occurrence of accidental cases of the mis-penetration of the anatomical structure and vital vessels around the lesion, such as the diaphragm, colon and small intestine, thus avoiding the incidence of pneumothorax, hydrothorax,colon fistula and intestinal fistula after radiofrequency ablation. In view of the above advantages, CEUS/CT fusion imaging technology facilitates precise liver radiofrequency ablation treatment, which has a high clinical value.
# Study on Mechanical Properties and Application of a New Flexible Bolt
## 6 Engineering Application
### Analysis and Discussion  
The surrounding rock damage after excavation shows a characteristic of circular distribution [26]. Macro damage would occur in the shallow surrounding rock and the fractures have certain openings. Accordingly, the shallow surrounding rock belongs to a strong fracture circle. Rock body damage would occur in the deep surrounding rock, but no noticeable fractures can be observed. Thus, the deep surrounding rock still possesses a strong bearing capacity, belonging to a weak fracture circle. There is no damage in the deeper surrounding rock. The rock body only deforms elastically and can transmit the stress continuously, thus belonging to an elastic circle. In this case, the bolt support should penetrate the fracture circles to locate the anchorage point within the elastic circle, thus realizing transboundary support. The large-scale displacement in the shallow surrounding rock can be restrained through the small-scale displacement in the deep surrounding rock, hence controlling the rock body damage and inhibiting fracture development in the surrounding rock [29; 30].  
Limited by the excavation height and construction equipment, the bolts are usually installed within the range of fracture circles in the actual construction practices. Accordingly, the surrounding rock damage would deteriorate to some degree. The innovative flexible bolt structure can solve this difficult problem. The flexibility of steel-wire rod body can guarantee the sufficient length of the bolts. Meanwhile, the bolt end can be preloaded with ball nut, which makes it unnecessary to prestress the steel-wire rod body with locksets. In this way, fast installation can be realized, saving up to 50% installation time.  
The fracture circles extend to 2.5 m deep in the roadway roof in this test and the roadway is 3200 mm high. In view of the existence of float coal on the roadway floor, the maximum length of rigid bolts can be no longer than 2.5 m, thus failing to meet the demand of transboundary support. By contrast, the length of flexible bolts reaches 4080 mm and the anchored segment is located in the elastic circle of the surrounding rock. In this case, the roof can become a thick anchored rock beam, improving the flexural rigidity of the roof to a noticeable degree. Meanwhile, the increase of anchorage depth makes it possible to reduce the number of flexible bolts needed, thus increasing the rate of bolt support. The preload of flexible bolts is set at 80 kN and the flexible bolts are in the quasi-elastic stage (Figure 6b). When minor deformation occurs in the roof surrounding rock, the flexible bolts can function rapidly to inhibit further development of fractures, which guarantees the integrity of roof as well as the excavation safety.
# Efficient Power Control Framework for Small-Cell Heterogeneous Networks
### Motivation and Contribution  
Though there has been considerable with regards to the development of efficient techniques for heterogeneous networks, the power controlling aspect of such networks has received little attention. This is particularly because small-cell base station (BS) is considered to be operating at all the time in the heterogeneous network, which may not be always true. We propose that small-cell BSs can be controlled efficiently to reduce the power consumption of such networks. Thus, to this end, our work provides the following key contribution to the state-of-the-art:  
1. We develop a heterogeneous network having multicast groups of user equipments (UEs) served by different small-cell BSs. The small-cell BSs can be controlled by macro-cell BS to switch on and off based on the network demands.
2. Due to the sparse nature of the formulated problem, a small-cell BS switching off mechanism has been introduced to control the power of the network. When the problem becomes infeasible, the admission control policy has been introduced to meet the quality of service requirements of the UEs in the network.
3. Detailed comparison with existing state-of-the-art approaches (coordinated beamforming and membership deflation) has been provided. The results show that the proposed power control solution outperforms the conventional techniques significantly in terms of reduction in power consumption and the capacity of admitted UEs.
# Quadratic Multilinear Discriminant Analysis for Tensorial Data Classification
## 4 Experimental Results
### Tensors from ECG Data
The tensorization process is summarized in Figure 3. We have a total of 24 healthy and 129 unhealthy patients, some of which have multiple tensors (arising from multiple recordings in the dataset). In each run of our experiments, we take half the patients from each class for training, and use the remaining ones for testing. The healthy ones for training and testing are oversampled, so that the training set has an equal number of healthy and unhealthy ones, as well as the testing set. For each of the 6 features of each of the 12 leads, we normalize across all samples and Taut-string parameter values use mean and standard deviation from the training data. We then classify the tensors using KNMDA and other tensor-based methods, or vectorize them and classify the vectorized tensors using linear SVM and linear LDA. For each of the MDA methods, we project onto the space \(\mathbb{R}^{5\times 5\times 5}\) which is the choice of dimension that seems to perform best as Tables 6 and 7 show. In KNNDA, SL-action was chosen for all modes, with imposed regularization with parameter \(\epsilon=1\) and a maximum of 10 iterations. We repeated each run 30 times and report AUC averages and standard deviations from each method in Table 8. We repeated the experiment after having added Gaussian noise to the signals (after the Butterworth filter and before applying the taut string method), with signal-to-noise ratios SNR of -10 and -30. Despite the low SNRs, all methods perform reasonably well because of the way we extract features via taut string and the fact that the multiple parameters for taut-string approximation encoded in the tensors act as a denoising tool, which is an interesting result. In the case of clean tensors, we also tested another manifold optimization method, ManPDA, but it performed worse than ManTDA with an average AUC of 0.85(0.09). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dimensions** & **[1,1,1]** & **[2,2,2]** & **[3,3,3]** & **[4,4,4]** & **[5,5,5]** \\ \hline AUC & 0.83 & 0.87 & 0.86 & 0.90 & 0.91 \\ \hline SD & 0.07 & 0.06 & 0.06 & 0.04 & 0.03 \\ \hline
**Dimensions** & **[6,5,4]** & **[6,5,5]** & **[6,5,6]** & **[6,5,7]** & **[6,5,8]** \\ \hline AUC & 0.90 & 0.90 & 0.76 & 0.57 & 0.60 \\ \hline SD & 0.05 & 0.05 & 0.17 & 0.13 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance (mean AUC and standard deviation) of CMDA on tensors from the PTB dataset as the dimensions \([I,J,K]\) increase. Figure 3: Construction of tensors from the PhysioNet PTB dataset for each patient.