esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results
### RAM\({}^{3}\)S vs. Samza  
Our final experiments aim to compare RAM\({}^{3}\)S with Samza. Indeed, the simplified interface offered by Samza to the developer (with respect to those of the other considered frameworks) make it a competitor of RAM\({}^{3}\)S, when it comes to consider abstracting the developer from the details of the underlying stream processing platform. It is thus desirable to compare the performance of RAM\({}^{3}\)S and Samza both in ease of use and in throughput.  
In the first experiment, we compare the number of code lines required to implement the face recognition use case (see Section 1.1) when using the different considered frameworks. As results in Table 5 show, Samza is very effective in reducing the developer burden, requiring the minimum number of code lines. However, this result is only valid when Samza is used in connection with the Kafka message broker: When using a custom connector, to abstract from the used message broker, the performance of Samza is the worst one. On the other hand, RAM\({}^{3}\)S is the second best, but is already independent of the used message broker, thus representing the most effective general framework.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Framework** & **Throughput (Images/s)** & **Throughput (KB/s)** \\ \hline Spark & 18.0 & 6120 \\ Storm & 17.3 & 5882 \\ Flink & 15.4 & 5236 \\ Samza & 18.4 & 6256 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison between the four considered frameworks for the plate recognition use case: normal image size (a) and large image size (b).  
Figure 10: Plate recognition use case: known **(a)** and unknown **(b)** plate.  
In the second experiment, we compared the throughput of using Samza alone or within RAM\({}^{\text{S}}\), again for the face recognition use case. As reported in Section 1.1, the throughput obtained when using RAM\({}^{\text{S}}\) on top of Samza is about 240 KB/s. The use of Samza alone using the custom connector allows to reach the throughput of about 280 KB/s, with a 15% improvement, while using a Kafka connector we obtain a further speed increase, reaching 320 KB/s, with a 35% improvement over the original case.  
The lesson we learned from this last experiment was twofold:  
1. The introduction of a custom connector to manage the input stream is slowing down the overall system;
2. The tight coupling between Samza and Kafka results in high performance (see Section 4.3).  
The system slowdown is clearly caused by this overlapping of levels: This is inevitable if we want to achieve generality in using any stream processing framework coupled with any message broker.
## 1 Introduction
Although the above literature review demonstrates that the existing GPR data fusion methods have achieved some progress in improving GPR detection resolution and depth, the following limitations still exist [21; 22; 23; 24]:  
1. The current research has only used a relatively simple weighting strategy to fuse multi-frequency GPR signals; its biggest drawback is that the high-frequency components of GPS signals are easily diluted and reduced in time-varying fusion local segments. 2. Most of the above studies usually fuse all of the profile information of different-frequency GPRs in one static window; it is difficult to keep the smoothness of the transition in a section when merging high-frequency and low-frequency GPR profiles, and thus is not suitable for fusion occasions when the overlaps of GPS signal bandwidths are not good enough. Currently, the typical data fusion methods for multi-frequency GPR mainly include wavelet transform, Fourier transform, S-transform and principal component transform. Compared with the other methods, the wavelet transform method has good advantages in signal processing and fusion for multi-frequency GPR data, and it is also widely used in the field of data fusion. However, the current research mainly uses a relatively simple weighting strategy to fuse wavelet transform signals; its biggest drawback is that the high-frequency components of the signal are easily diluted, which results in the loss of high-resolution reflection signals. For these reasons, this paper presents a novel multi-frequency GPR data-fusing method via sliding window and wavelet transform weighting joint to accurately detect top-coal structures, integrating the advantages of high- and low-frequency GPR signals. The main contributions of this work are summarized as follows [25; 26; 27; 28]:1. A joint sliding window and wavelet transform-weighting method is proposed for multi-frequency GPR data fusion that can integrate multi-frequency GPR signals into one composite profile with higher resolution and greater detection depths. 2. A dynamic sliding window is designed to determine the time-varying weight values of each frequency GPR signal according to the wavelet energy proportion within the sliding window. 3. A reflection intensity model of multi-frequency GPR signals traveling in the top coal is established, which can be used to identify the interface information between the coal-gangue-rock and can also effectively display the detailed internal structure of top coal in one composite profile. The overall framework of this work is elaborated in Figure 1. First of all, the spatial correspondences of different frequency GPR data are established based on the data preprocess and spatial alignment. Secondly, the multi-frequency GPR data are fused through a sliding window and wavelet transform-weighting method. Finally, a reflection intensity model of multi-frequency GPR signals is established to recognize the detailed internal structures of the top coal.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 5 Experiments and Results
### Attack Simulation
#### 5.2.1 Batch Time-Out Attack
As such, setting the batch timeout beyond 5 s not only increased the latency but also risked falling below performance thresholds that could breach Service Level Agreements (SLAs) or cause network unavailability for applications such as supply chain tracking or financial settlement.
## 3 Results
### Adaptability Assessment Tools and Models
#### 3.1.8 Adaptive Reuse Assessment Model (ARAM), 2021  
Adaptive Reuse Assessment Model (ARAM) [49] is an assessment model of adaptive reuse and adaptation potential in heritage buildings at different life cycle stages. The model relies on identifying challenge categories for adaptive reuse of heritage buildings and critically analyzing existing decision-making tools and assessment models [49]. The authors identify 32 challenges related to the adaptive reuse of heritage buildings categorized into eight groups: environmental, social, economic, legal, political, physical, locational, and technical. The ARAM model is based on two previous models: [PERSON]'s model of decision-making in building adaptation [47] and the PAAM assessment model for office buildings, as it relies on a comprehensive database for important attributes of adaptation projects. However, in the ARAM model, authors consider modifying the sequence and importance of attributes' categories to fit the value-representative characteristics of heritage buildings and the legal obligation to preserve them. They also considered adding the political category, and heritage value and authentic features not addressed by previous frameworks. The assessment methodology starts with identifying the key values, which are the ones corresponding to heritage significance and authenticity, then confirms that the adaptive reuse actions are capable of preserving those. The next step is to consider the economic viability, followed by the technical and legal considerations. The model does not view the remaining categories, i.e., locational, physical, and environmental challenges. The ARAM model was applied to two illustrative case studies of heritage-listed city halls in Queensland, Australia, to demonstrate its practical application and test its validity. The case studies' findings suggest that the physical category is the third most important challenge to adaptive reuse, which does not correspond to the reviewed literature findings. This finding resulted in alteration in the initial model by placing the physical category at the last stage after the legal consideration.  
The ARAM presents a simple model of assessment that can be carried out by stakeholders regardless of their level of expertise. The model does not suggest technical solutions. It rather acts as a decision-support tool for involved stakeholders.
## 2 Materials and Methods  
### Materials  
We use polygon-delineated boundaries of Finnish crop field parcels illustrated in Figure 1, collocated with mosaics of SAR and MSI satellite images over a time period from 11-23 April 2018 from Copernicus Sentinel-1 and Sentinel-2 missions, where the parcels are classified to one of multiple crop field tillage operations that affect, for example, soil properties and nutrient runoff to surface waters. The illustration reveals that the parcels have complex shapes and varying sizes, and that many of the parcels are small.
## 4 Analysis and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Conditions** & **Data Set** & **Number of Samples** & **Fault Location** \\ \hline \multirow{4}{*}{1} & Bearing 1\_1 & 123 & outer ring \\  & Bearing 1\_2 & 161 & outer ring \\  & Bearing 1\_3 & 158 & outer ring \\  & Bearing 1\_4 & 122 & bearing cage \\  & Bearing 1\_5 & 52 & inner ring, outer ring \\ \hline \multirow{4}{*}{2} & Bearing 2\_1 & 491 & inner ring \\  & Bearing 2\_2 & 161 & outer ring \\  & Bearing 2\_3 & 533 & bearing cage \\  & Bearing 2\_4 & 42 & outer ring \\  & Bearing 2\_5 & 339 & outer ring \\ \hline \multirow{4}{*}{3} & Bearing 3\_1 & 2538 & outer ring \\  & Bearing 3\_2 & 2496 & inner ring, outer ring, \\ \cline{1-1}  & Bearing 3\_3 & 371 & inner ring \\ \cline{1-1}  & Bearing 3\_4 & 1515 & inner ring \\ \cline{1-1}  & Bearing 3\_5 & 114 & outer ring \\ \hline \hline \end{tabular}
\end{table} 
Table 8: XJTU-SY bearing data set information. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**SNR/dB**} \\ \hline  & \(-\mathbf{10}\) & \(-\mathbf{8}\) & \(-\mathbf{6}\) & \(-\mathbf{2}\) & \(\mathbf{2}\) \\ \hline
1 & 68.30\% & 67.54\% & 76.44\% & 83.55\% & 94.58\% \\
2 & 69.15\% & 69.22\% & 79.63\% & 85.11\% & 95.37\% \\
4 & 71.40\% & 72.71\% & 80.20\% & 91.32\% & 97.10\%  
The diagnostic results for different signal-to-noise ratios are shown in Figure 17. When the signal-to-noise ratio (SNR) is \(-10\) dB, the LeNets, CNNs, and ResNets achieved accuracy rates of 47%, 61%, and 63%, respectively. In contrast, our proposed method in this paper achieved an accuracy rate of 64%, outperforming the LeNets by 17%, CNNs by 3%, and ResNets by 1%. To sum up, the proposed method shows strong generalization ability under the validation of new data sets.
## 4 Cryptography for Web Services  
WS-Security is a standard that describes how messages using the SOAP protocol can have integrity, authentication, and confidentiality. WS-Security is a flexible protocol that supports different formats of authentication security tokens, different encryption technologies, and different signature formats. WS-Security does not define new security mechanisms, but it leverages existing standards such as XML Encryption, XML Signature, and Security Tokens e.g., Kerberos Tickets and X.509 certificates. We have described this standard in the form of a pattern:  
**WS-Security [21]:** Defines how to secure SOAP messages applying XML security technologies such as XML Encryption and XML Signature. It also defines how to embed different security tokens. Security tokens provides authentication by proving one's identity (certificates or SAML assertions are examples).  
Web services that exchange XML messages can be target of attacks. Some security standards have been developed to apply mechanisms that reduce security risks, one of these is. XML Signature. This standard is a joint effort between the World Wide Web Consortium (W3C) and the Internet Engineering Task Force (ITEF). XML Signature defines how to digitally sign an entire XML message, part of an XML message, or an external object. XML Signature also includes hashing, but the pattern name follows the name of the standard. Because XML documents can have the same contents but in different layouts, we need to convert the documents into a canonical form before we apply digital signatures. Note that XML Signature solves the same problem as the Digital Signature with Hashing pattern but in a more specialized context. The XML Signature pattern, a specialization of the Digital Signature with Hashing, is used to secure XML messages. We first defined three basic patterns to describe some cryptographic aspects:  
**Symmetric Encryption [22]:** protects message confidentiality by making a message unreadable to those that do not have access to the key. Symmetric encryption uses the same key for encryption and decryption.  
**Digital Signature with Hashing [23]:** allows a principal to prove that a message was originated from it. It also provides message integrity by indicating whether a message was altered during transmission.  
**XML Encryption [22]**  
This standard describes a process to apply encryption functions to data, keeping a correct XML syntax.  
**XML Signature [23]**  
Allows a principal to prove that a message was originated from it. It also provides message integrity by defining whether a message was altered during transmission. The XML Signature standard [24] describes the syntax and the process of generating and validating digital signatures for authenticating XML documents. XML Signature also provides message integrity. It requires canonicalization before hashing and signing.
# Matching of Water Breakthroughs in a Low-Resistivity Oil Reservoir Using Permeability Anisotropy
## 1 Introduction  
### Low Resistivity Reservoirs  
Low resistivity in oil-bearing zones can lead to low contrast with water-bearing zones, resulting in water saturation above 50%, due to which they can be overlooked and bypassed [1]. For example, no resistivity contrast between oil and water zones was observed for the reservoirs in Sudan which were bypassed in the past [2]. Low resistivity and low contrasts can be associated with water salinity, clay type, micro-porosity, the presence of conductive minerals, anisotropy, bed thickness, bed dip, drilling mud invasion, and logging tool resolution [1; 3; 4; 5]. High-salinity interstitial water causes low resistivity within the pay zone, while low-salinity water can cause low contrast in the pay zone.  
The main features of a low-resistivity pay zone are abundant microporosity and high irreducible brine saturation resulting from cementation, clay coating, or clay bridging within pores and in fine-grained sandstones [1; 3]. Due to the lack of vertical resolution, the tools provide an average resistivity measurement over the bedded sequences with laminar shales. With dispersed clay, the irreducible water saturation increases, which can dramatically reduce resistivity. The actual water saturation can be high but water-free hydrocarbons are produced due to micro-porosity [4]. In the presence of conductiveminerals such as metal sulfides, the calculated water saturation can be overestimated compared to the amount of water produced during hydrocarbon production. However, the causes of low resistivity can differ from reservoir to reservoir or between particular depositional environments and may not be directly applicable to other reservoirs where conditions initially appear to be similar [5]. Thus, uncertainty in low-resistivity pay zones can arise at the initial phase of the evaluation of water saturation before the start of production.
# Behavioural Realism and Its Impact on Virtual Reality Social Interactions Involving Self-Disclosure
###### Abstract  
This study investigates how the behavioural realism of avatars can enhance virtual reality (VR) social interactions involving self-disclosure. First, we review how factors such as trust, enjoyment, and nonverbal communication could be influenced by motion capture technology by enhancing behavioural realism. We also address a gap in the prior literature by comparing different motion capture systems and how these differences affect perceptions of realism, enjoyment, and eye contact. Specifically, this study compared two types of avatars: an iClone UNREAL avatar with full-body and facial motion capture and a Vive Sync avatar with limited motion capture for self-disclosure. Our participants rated the iClone UNREAL avatar higher for realism, enjoyment, and eye contact duration. However, as shown in our post-experiment survey, some participants reported that they preferred the avatar with less behavioural realism. We conclude that a higher level of behavioural realism achieved through more advanced motion capture can improve the experience of VR social interactions. We also conclude that despite the general advantages of higher motion capture, the simpler avatar was still acceptable and preferred by some participants. This has important implications for improving the accessibility of avatars for different contexts, such as therapy, where simpler avatars may be sufficient.  
behavioral realism; avatars; motion capture technology; perceived realism; avatar enjoyment; avatar preferences; self-disclosure; metaverse +
Footnote †: journal: Applied sciences
## 4 Solutions and Results  
In this section, OHAM is applied to non-linear system of ordinary differential Equations (10) and (11). The homotopy of Equation (10) is constructed as:  
\[(1-q)(h^{{}^{\prime\prime\prime}}(\lambda))-(qC_{1}+q^{2}C_{2})(h^{{}^{\prime \prime\prime}}(\lambda)-\frac{2m}{(m+1)}h^{{}^{\prime}}(\lambda)^{2}+hh^{{}^{ \prime\prime}}(\lambda)+\lambda_{1}(2h^{{}^{\prime}}(\lambda)+\lambda h^{{}^{ \prime\prime}}(\lambda))\]  
\[+2\Big{(}\frac{m}{m+1}+\lambda_{1}\Big{)}(1-\mu)^{2.5}+\tau^{4}\Gamma Sin\frac{ \chi}{2}\Theta-\tau^{2}(M+\lambda)(h^{{}^{\prime}}(\lambda)-1)=0, \tag{33}\]  
we consider \(h(\lambda)\) as follows:  
\[h(\lambda)=h_{0}(\lambda)+qh_{1}(\lambda) \tag{34}\]  
By solving Equations (33) and (34), equating based on some exponents of \(q\) and after some simplifications, we obtain the following 0 th order and 1 st order equations:  
\[q^{0}:h_{0}^{{}^{\prime\prime\prime}}(\lambda)=0, \tag{35}\]  
\[q^{1}:\,-\frac{2m(1-\mu)^{2.5}C_{1}}{m+1}-2(1-\tau)^{2.5}\lambda_{1}C_{1}- \lambda\tau^{2}C_{1}-M\tau^{2}C_{1}-\gamma\tau^{4}Sin\Big{(}\frac{\chi}{2} \Big{)}C_{1}\Theta_{0}(\lambda)\] \begin{table}
\begin{tabular}{c c c c c} \hline \(z\) & \(g(z)_{\text{Exact}}\) & \(g(z)_{\text{OHAM}}\) & \(|\,g(z)_{\text{Exact}}-g_{2}(z)_{\text{OHAM}}\,|\) & \(|g(z)_{\text{OHAM}}-g_{7}(z)_{\text{HPM}}\,|\)[56] \\ \hline \(0.1\) & \(-0.099\) & \(-0.09900630502616097\) & \(6.3\times 10^{-6}\) & \(1.7\,\times\,10^{-4}\) \\ \hline \(0.2\) & \(-0.192\) & \(-0.19201411767774168\) & \(1.4\times 10^{-5}\) & \(3.3\,\times\,10^{-4}\) \\ \hline \(0.3\) & \(-0.273\) & \(-0.273023380384165\) & \(2.3\times 10^{-5}\) & \(4.9\,\times\,10^{-4}\) \\ \hline \(0.4\) & \(-0.336\) & \(-0.3360322825715557\) & \(3.2\times 10^{-5}\) & \(6.3\,\times\,10^{-4}\) \\ \hline \(0.5\) & \(-0.375\) & \(-0.3750381526285426\) & \(3.8\times\,10^{-5}\) & \(7.5\,\times\,10^{-4}\) \\ \hline \(0.6\) & \(-0.384\) & \(-0.38403888493553096\) & \(3.9\,\times\,10^{-5}\) & \(8.5\,\times\,10^{-4}\) \\ \hline \(0.7\) & \(-0.357\) & \(-0.3570341025474121\) & \(3.4\times 10^{-5}\) & \(8.9\,\times\,10^{-4}\) \\ \hline \(0.8\) & \(-0.288\) & \(-0.2880251968406201\) & \(2.5\times\,10^{-5}\) & \(8.4\,\times\,10^{-4}\) \\ \hline \(0.9\) & \(-0.171\) & \(-0.17101383385516783\) & \(1.3\times 10^{-5}\) & \(6.1\,\times\,10^{-4}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Absolute error comparison of exact solutions with 2 nd order OHAM solutions of \(g(z)\) and 7 th order solutions of HPM.
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
## 1 Introduction
### Contributions
Section 2 delves into the 3D sensing model for the single base station responsible for both transmission and receptionin the ISAC network, elaborating on its key components and operational principles. In Section 3, a cellular-like topological structure for 3D sensing in a multi-BS low-altitude networking scenario is proposed, highlighting its innovative features and advantages in the low-altitude environment. Section 4 showcases several experimental results and an in-depth analysis of the models, validating the effectiveness and performance improvements of the proposed models. Finally, Section 5 summarizes the research, drawing conclusions and pointing out potential directions for future work.
## 3 Results and Discussion  
### Longitudinal Electrical Conductivity  
The CFRP piles exhibit high electrical conductivity in the longitudinal direction due to the long and continuous nature of fibers. Using the procedure detailed in Section 2.1, the frequency-dependent longitudinal conductivity (\(\sigma_{L}\)) of a unidirectional CFRP lamina in the 60 Hz to 50 GHz frequency range was calculated. The results are compared with experimental results obtained by [PERSON] et al. (2018) and are shown in Figure 11a. At frequencies above 0.5 GHz, the inductive reactance becomes notably pronounced, resulting in an overall reduction in conductivity. A slight variation in the location of the point where conductivity begins to reduce is observed with changes in ply size, as depicted in Figure 11b. Lamina with more fibers exhibit an earlier onset of the declining trend, while those with lesser fibers experience this decline at a later stage. This behavior can be attributed to the inverse relationship between the overall inductance of a ply in the longitudinal direction and the number of fibers within the ply, coupled with a direct proportionality to the ply length. Figure 12 depicts the overall inductance \(\left(L_{f}\right)_{net}\) as a function of total number of fibers \(N\), for a 1 cm long ply.  
The decrease in overall inductance is a consequence of two primary factors. First, the effective inductance significantly reduces when more fibers are connected in parallel. Second, with an increasing number of fibers, the influence of mutual induction becomes negligible due to the cancelation of magnetic fields. Beyond \(\sim\)5000 fibers, the net inductance saturates, which is attributed to the limited influence of distant fibers on the inductance of a given fiber.  
Figure 10: Waveguide model schematic showing the composite sample under study.  
For the case of a 10 mm \(\times\) 10 mm \(\times\) 0.25 mm ply with a 60% fiber volume fraction, comprising roughly 70,000 fibers, an overall inductance of \(\sim\)2.5 pH was obtained. This value translated to an average fiber inductance of \(\sim\)17.5 \(\upmu\)H, which falls within the range published by other researchers [36]. Subsequently, employing this result, the frequency-dependent longitudinal conductivity \(\sigma_{L}\) can be calculated. The response obtained is cross-referenced with values documented in existing literature to validate the model [35; 37]. [PERSON] et al. [35] conducted experiments to investigate the AC conductivity of USN-150R prepregs with fiber conductivity between 600-660 S/cm. Their findings confirm the accuracy and technique of the model proposed in this study, as depicted in Figure 11a.  
Figure 11: (**a**) Longitudinal CFRP conductivity declining at higher frequencies due to the rise of inductive reactance and (**b**) shift in conductivity decline point for different square ply sizes showing an early decline in conductivity in larger plies.