esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Design of Rock-Breaking Mechanism and Parameters
#### 2.2.3 Distance Between Each Group of Boreholes  
In shallow hole detonation, where the rock body is moved to a deep hole uncharged position and with free surface movement, the bottom of the rock body is subjected to large clamping force, and crushing and throwing movement is difficult. Based on the rock blasting damage criterion, the theoretical formula for the radius of the explosive crushing zone and fissure zone in the case of shallow hole blasting without coupled charging is [23]:  
\[R_{1}=\left(\frac{\rho_{0}D_{e}^{2}nK^{-2}\gamma l_{e}A}{8\sqrt{2}\sigma_{cd} }\right)^{\frac{1}{2}}r_{b} \tag{6}\]  
\[R_{2}=\left(\frac{\sigma_{R}A}{\sqrt{2}\sigma_{td}}\right)^{\frac{1}{2}}R_{1} \tag{7}\]  
\[A=\sqrt{\left[\left(1+B\right)^{2}+(1-B^{2})-2\mu_{d}(1-\mu_{d})(1-B)^{2} \right]} \tag{8}\]  
where \(R_{1}\) and \(R_{2}\) are the radii of the crushed zone and fissure zone, respectively; \(\alpha\) and \(\beta\) are the load attenuation index; \(\rho_{0}\) is the density of explosives; \(D_{e}\) is the explosive detonation velocity; \(r_{b}\) is the radius of the blast hole; \(\mu_{d}\) is the dynamic Poisson's ratio, which can be taken as 0.8 times the static [PERSON]'s ratio; \(B\) is the lateral stress coefficient; \(K\) is the coefficient of uncoupling; \(n\) is the coefficient of increase in pressure of the explosive products; \(l_{e}\) is the axial uncoupling coefficient of charge; \(\gamma\) is the bombardment product expansion adiabatic coefficient; \(\sigma_{cd}\) and \(\sigma_{td}\) are the dynamic uniaxial compressive and tensile strength of the rock body; and \(\sigma_{R}\) is the radial stress at the interface between the crushing zone and fracture zone.  
In order to achieve better blasting and rock-breaking effects, the spacing \(r_{\text{q}}\) between the deep and shallow holes in each group should be controlled within the range of the blasting fissure zone, that is, to meet the following equation:  
\[r_{\text{q}}\leq R_{2} \tag{9}\]
# Comparison of Fatigue Performances Based on Shape Change of Rail Fastening Spring
## 3 Comparison of Fatigue Performance Based on Fastening Spring Shapes
### Spring Diameter  
The effect of the spring diameter on fatigue performance is evaluated in this section. In this analysis, six different diameter variables (12D, 13D, 14D, 16D, 17D, and 18D) were compared to the original spring (15D) used in current design practice, where 15D means the diameter of 15 mm. Figure 14(a) shows the fastening force vs. displacement diagram for the spring widths. It can be observed that the required vertical displacements corresponding to the fastening force (9 kN) could be reduced as the spring diameter increased. Figure 14(b) shows that the ratio of the clamping force at the torque to the rail fastening force is \(\sim\)60% (for the model of \(+15\%\)), which is similar to Figure 13(b). This ratio was about the same for the other models. The shape with a 12 mm diameter could not achieve the rail fastening force of 9 kN during the initial fastening; thus, it was excluded from fatigue analysis.  
Figure 15(a) shows the maximum principal stresses at the fastening force of 9 kN for various diameters, while Figure 15(b) shows the stress range. The maximum principal stress during the initial fastening decreased as the diameter increased. In contrast, the stress range caused by the vertical deflection increased. Lower mean stress and stress amplitude are always preferable for achieving a better fatigue performance. However, the results showed that the larger diameter reduced the mean stress but increased the stress amplitude. Therefore, the optimum diameter can be determined by the margin of safety based on the modified Goodman diagram. Figure 17 shows the fatigue performance comparison based on the analysis results in Figure 16. The results presented safety margin ratios of 54%, 66%, 75%, 78%, 80%, and 81% for 13, 14, 15, 16, 17, and 18 mm diameters, respectively.  
Figure 14: Comparison of the fatigue performances when varying the spring width: (a) fracture criteria; (b) safety margin (%).  
Figure 15: Force vs. displacement diagram: (a) fastening force vs. displacement when varying the spring diameter; (b) comparison of clamping force at the torque with rail fastening force (for the 15D model).  
These results indicated that the decreased mean stress for the thicker spring diameter could secure a larger margin of safety, despite an increased stress amplitude. Therefore, a better fatigue performance was expected from the thicker diameter of the fastening spring.
# Supporting Privacy of Computations in Mobile Big Data Systems +
## 6 Privacy Preserving Search for the Nearest Bank/ATM
### Server-Side and Client-Side Cost  
Figure 13 shows the network traffic as a function of the number of servers (\(n\)) involved in the creation of the garbled circuit that can compute the nearest ATM and the corresponding distance in a privacy preserving manner. For example, with \(n=4\) servers, the servers exchange a total of 126 \(GB\) of information to create the garbled circuit. This result demonstrates the feasibility of our approach for performing real-world privacy-preserving computations.  
In order to facilitate the creation of the garbled circuit, and for the evaluation, the mobile client sends: (i) the seed values to the \(n\) servers; and (ii) the garbled values representing the coordinates  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Block** & **\#\(XOR\) Gates** & **\#\(AND\) Gates** \\ \hline \(CMP\) & \(3(l+1)\) & \(l+1\) \\ \(MUX_{min}\) & \(2(l+1)\) & \(l+1\) \\ \(MUX_{index}\) & \(2L_{ind}\) & \(L_{ind}\) \\ \(Total\) & \(5(l+1)+2L_{ind}\) & \(2(l+1)+L_{ind}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Circuit size of 1 Min block.  
Figure 13: Server-side network traffic to construct the garbled circuit for determining the nearest ATM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Block** & **\#\(XOR\) Gates** & **\#\(AND\) Gates** \\ \hline \(DIST(L)\) & \(v_{1}=(15l+1)L\) & \(v_{3}=4 IL\) \\ \(T(MIN)\) & \(v_{2}=[5(l+1)+2L_{ind}]\times(L-1)\) & \(v_{4}=[2(l+1)+L_{ind}]\times(L-1)\) \\ \(Total\) & \(v_{1}+v_{2}\) & \(v_{3}+v_{4}\) \\ \(Application\) & 2596 & 854 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Complete circuit size for nearest ATM. \(DIST(L)\) denotes distance to \(L\) locations. \(T(MIN)\) denotes the tree of _MIN_ blocks.  
of the input location to the evaluator. Since the ATM locations are publicly known, they are assumed to be hard-coded in the garbled circuit. _i.e._, the client is not required to transmit the ATM locations to the servers.  
Figure 14 shows the total number of bits generated by the client to delegate the privacy preserving computation of finding the nearest ATM and the distance from the client. To preserve location privacy, the client exchanges a very small amount of information with the servers--less than 60 Kbits, with \(n=4\) servers, for example. From Figures 13 and 14, we note that in comparison to the server-side cost, the client-side cost grows much slowly with the number of servers.
# Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles
## 1 Introduction
Previous studies have been focused on the analysis of spectro-temporal characteristics of reptile calls, but to the best of our knowledge, there has not been any research that has used their acoustic signals for automatic inter-species classification. For this reason, this paper proposes a novel approach of taxonomic identification of reptiles through their acoustic features. To achieve this goal, Linear Frequency Cepstral Coefficients (LFCCs) and MFCCs [17] have been used to parametrize the reptile acoustic signals and they have been fused to obtain a robust characterization of the signal in the frequency domain. In addition, two widely used machine learning algorithms--K-Nearest Neighbors (kNN) [18] and Support Vector Machine (SVM) [19] have been utilized to verify the robustness of the proposed parameters. The approach has been validated in three public collections of reptile sounds selected by experts, which contain 27 different species with several types of calls. Therefore, despite the small corpus, this study may serve as a first reference in the field of automatic acoustic recognition of reptile specimens for researchers. The remainder of this paper is organized as follows. Section 2 presents the proposed technique where the audio signal segmentation and the feature extraction procedure are described. Two classification systems based on kNN and SVM algorithms are described in Section 3, particularized for acoustic recognition. The experimental methodology, the sound dataset, and the results obtained are shown in Section 4, where a comparison of features and classification algorithms is done. Finally, the conclusions and future work are shown in Section 5.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 3 Latency-Inducing Receiver Digital Baseband
### Performance Figure Derivation
#### 3.2.2 Channel Equalizer  
The discussed RX DBB employs single carrier frequency domain equalization (SC-FDE). However, before neutralizing the channel effects, the received symbols must be transformed to the frequency domain. Once a block of 448 data symbols and its 64-symbol long cyclic prefix (CP) have accumulated at the input of the channel equalization block, they are transferred to the frequency domain using the same 512-point FFT [35] component, as described in Section 3.2.1. Multiplying the inverse of the CFR with a block of received symbols in the frequency domain, the equalization itself takes the form of parallel complex multiplications. Executing the multiplication and corresponding summations alleviates most of the time delays, making the FFT the main factor contributing to latency. Lastly, the 512 equalized symbols are transferred back to the time domain by an inverse fast Fourier transform (IFFT). Given the same processors are often used for both time-to-frequency and frequency-to-time domain transformation, the same performance metrics are associated with both the FFT and the IFFT component. Thus, the total propagation delay of the channel equalization block is that of two FFT/IFFT components. Its throughput is limited by that of a single FFT/IFFT component.
# Knowledge Graph Alignment Network with Node-Level Strong Fusion
## 2 Related Work  
Early entity alignment required substantial human involvement to design different features [12] and rules [13] for each task, or by importing external data sources [14, 15] to align. This kind of method is limited by cost and difficult to reuse, simultaneously. The large KGs will have greater difficulties to achieve alignment.  
Most of the existing entity alignment methods are based on embedding, TransE is one of the main technologies for KGs embedding. MTransE was the first method to extend the idea of the translation model into the field of cross-lingual entity alignment, designing three cross-lingual transformations of entities and relations. IPTransE jointly encoded entities and relations of different KGs into a unified semantic space through a small set of aligned entity seeds, and proposed an interactive strategy to expand the seed sets. Later BootEA transformed entity alignment into a classification problem and designed a bootstrapping process to reduce error accumulation by alignment editing method. Such methods rely on pre-aligned seed sets and cannot model complex relationships between entities.  
Graph convolutional network (GCN) is another major technique for KGs embedding and has made significant progress in recent years. GCN-Align is the first method to use graph convolutional neural network for entity alignment, with entities of each language being embedded into a unified vector space through GCNs. While traditional GCN ignores the relations in KGs, Relational Graph Convolutional Networks (RGCN) [16] improved GCNs by specific matrix coding for each relation. But it also introduced a large number of parameters, which greatly increase the requirements on computational power. Vectorized relational graph convolutional network (AVR-GCN) [17] extended RGCN with similar translation feature. RDGCN constructed dual relation graphs to learn relation information in KGs. AliNet learned the structure of entities through GCNs and attention mechanism, alleviating the structure heterogeneity of KGs. NMN designed a graph sampling strategy to select more important entity neighborhoods and discovered new aligned entities through graph matching. The joint learning (HGCN) method [18] conducted joint learning on the structure and relation of entities, and performed relation alignment in an unsupervised manner. Attributed Graph Neural Network (AttrGNN) [19] learned the structure and attribute of entities respectively by dividing sub-graphs. However, the information union in these methods is still at the aspect level and the operation is relatively rough, resulting in the original advantages being consumed in the process of information union.  
However, the information union in these methods is still at the aspect level and the operation is relatively rough, resulting that the original advantages will be consumed in the process of information union. Therefore, a fine fusion at the node level is proposed in this paper to greater play to the characteristics of various aspects of entities and enhance entity alignment.
# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 4 Data
### Basic Statistics
#### 4.3.1 Hourly Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Minimum** & **Maximum** & **Average** & **Median** & **1 st** & **3 rd** \\  & & & & **Quartile** & **Quartile** \\ \hline temperature (°C) & \(-\)18.0 & 39.4 & 12.9 & 14.1 & 3.5 & 22.6 \\ amount of water precipitation (mm) & 0.0 & 71.0 & 0.2 & 0.0 & 0.0 & 0.0 \\ humidity (\%) & 7.0 & 100.0 & 59.7 & 60.0 & 45.0 & 76.0 \\ vapor pressure (hPa) & 0.5 & 36.1 & 11.2 & 9.0 & 4.1 & 17.2 \\ dew point temperature (°C) & \(-\)31.1 & 27.2 & 4.4 & 5.5 & \(-\)5.3 & 15.1 \\ atmospheric pressure (hPa) & 975.1 & 1028.3 & 1006.0 & 1006.1 & 999.7 & 1012.2 \\ sea-level pressure (hPa) & 984.4 & 1039.6 & 1016.2 & 1016.3 & 1009.6 & 1022.8 \\ hours of sunshine (hr) & 0.0 & 1.0 & 0.3 & 0.0 & 0.0 & 0.6 \\ solar radiation quantity (M/m2) & 0.0 & 3.6 & 0.5 & 0.0 & 0.0 & 0.9 \\ amount of snowfall (cm) & 0.0 & 28.5 & 0.2 & 0.0 & 0.0 & 0.0 \\ total cloud cover (decile) & 0.0 & 10.0 & 4.0 & 3.0 & 0.0 & 8.0 \\ ground-surface temperature (°C) & \(-\)15.0 & 65.1 & 14.4 & 14.4 & 2.1 & 24.0 \\ wind speed, x-component (m/s) & \(-\)10.7 & 7.9 & 0.3 & 0.5 & 0.0 & 1.1 \\ wind speed, y-component (m/s) & \(-\)10.7 & 10.1 & \(-\)0.8 & \(-\)0.9 & \(-\)2.5 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Basic statistics for hourly data in mountainous area (Daegwalleeong).
# Synthesis of Sources of Common Randomness Based on Keystream Generators with Shared Secret Keys
## 7 Computational Complexity and Overhead Analysis
### CR Generation Phase  
The CR block performs bitwise modulo-2 addition (XOR) between the local Bernoulli-generated sequence \(X\) and the keystream \(C_{K}\) from \(KSG(K_{G})\), see (1), requiring n XOR operations per data block of length \(n\). The generation of \(X\) involves \(n\) calls to a local random number generator (RNG), while the \(KSG(K_{G})\) satisfies the avalanche criterion and typically requires a fixed, small number of operations per bit depending on the \(KSG\) implementation. Table 3 provides a structured comparison of widely used \(KSG\)s that can be employed within SKD systems, focusing on their operational complexity, memory footprint, and throughput performance under practical conditions.  
The table includes:  
* AES in CTR/OFB mode, representing a high-security, block-cipher-based KSG with higher per-bit cost in pure software but highly efficient under AES-NI [44, 45, 46].
* Lightweight stream ciphers (Grain v1 [47, 48], Trivium [48, 49]), illustrating extremely low per-bit complexity and minimal memory requirements suitable for resource-constrained SKD embedded implementations with minimal RAM and low power.
* ChaCha20 [50, 51] and HC-128 [48, 52], demonstrating high throughput and balanced security for SKD systems eliminating the need for hardware accelerators.  
Based on these findings, we conclude that for autonomous SKD with \(KSG(K_{G})\):  
* AES remains suitable when hardware AES-NI support is available, ensuring low latency and secure keystream generation.
* Grain/Trivium may be preferable in microcontroller-based SKD nodes due to minimal computational complexity and energy usage.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**KSG** & **Block Size** & **Ops per Bit (SW)** & **Ops per Bit (HW/AES-NI)** & **RAM Footprint** & **Throughput (MB/s, SW)** & **Remarks** \\ \hline AES (CTR/OFB) & 128 bits [44, 45, 46] & \(\sim\)10–15 ops/bit & \(\sim\)10 op/bit & \(\sim\)1 op/bit (tables) & \(\sim\)2–4 KB [47, 48] & \(\sim\)30–100 MB/s & High security margin, mature \\ Grain v1 [47, 48] Trivium [48, 49] & 1 bit & \(\sim\)1–3 ops/bit & \(\sim\)1 op/bit & \(\sim\)80 bytes & \(\sim\)50–150 MB/s & Lightweight, low area \\ ChaCha20 [50, 51] & 512 bits & \(\sim\)3–4 ops/bit & \(\sim\)1–2 ops/bit & \(\sim\)128 bytes & \(\sim\)50–200 MB/s & Lightweight, low area \\ HC-128 [48, 52] & 128 bits & \(\sim\)4–6 ops/bit & \(\sim\)2 ops/bit & \(\sim\)4 KB & \(\sim\)70–200 MB/s & High performance, strong security \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of stream cipher keystream generators for SKD systems.
# An Overview of Fog Data Analytics for IoT Applications
## 2 Integration of Fog Computing and IoT
### Fog Layer
#### 2.2.1 Fog Agent  
The fog agent handles the entire fog node management, which holds core functionality modules such as virtualization, network management, and resource allocation and scheduling. Physical resources are abstracted to the upper layers and provide support for creating virtual hardware components and environments for running processes and services in the node. Due to this mechanism, it is easy to allocate resources according to the processing needs at the run-time by creating virtual machine instances on top of the virtualized infrastructure with the help of HyperVisor. Virtual machines are created to host various services and applications to serve the IoT data processing needs with a dynamic allocation of required virtualized hardware. This functionality helps to efficiently allocate and scale processing needs to certain limits using resources in the node.  
Fog has inherent challenges regarding networking, whether inter-VM networking or with an external device. For that matter, the agent has networking modules that elegantly handle complex networking tasks and provides an abstraction layer, simplifying it further. It uses VNFs to provide network services with benefits of hardware independence, a high resilience, quick replacement, and easy configuration and deployment. From recent advancements in networking, it can also be customized to use case-specific networking modes to improve the throughput and reduce the latency in data packet communication.  
A fog node can deal with many simultaneous connections demanding various resources and services for different tasks. In that situation, the fog agent acts as a resource allocator and manager for serving virtualized resources and de-allocating when the task is finished. The agent includes service orchestration functionality and policies for life-cycle management with a global messaging bus to send control signals for synchronization. It also deals with secure inter-process communication and consistent data resource sharing between different VMs. Various resource allocation strategies are implemented in the resource management module. These should be selected carefully because allocating resources efficiently is necessary to reduce latency in communication.  
In addition, the fog agent also provides APIs to satisfy programmability needs at a low level in the node architecture. This feature supports the customization of fog nodes to accommodate functionalities such as real-time data synchronization between multiple devices, defining QoS, creating custom computing policies, and centralized control with mobile devices.  
Figure 3: Architecture of fog node.
## 5 Methodology
### Methods
#### 5.2.2 Extra Guard Time  
During the experiments involving the repeated and consecutive transmission of various symbols, the distribution of events at the receiver side became overlapped. This adverse effect could have resulted in a poor performance for the OCC link. Consequently, we introduced an additional extra guard time, comprising one symbol period with no present signal, after every transmitted symbol. The distribution of events with this extra guard time showed an improvement in terms of separability. The symbols constructed with the extra guard time and the use of the start frame delimiter are illustrated in Figure 8.  
Figure 7: In the upper image, we can observe the start frame delimiter at the transmitter side, while the lower image displays the data captured by the event camera.
enhances achievable data rates but declines with distance due to diminishing received power. At 100 km, a 50 MHz bandwidth supports up to 4500 Mbps, while at 3000 km, capacity drops to around 300 Mbps. The outage probability analysis shows that higher signal-to-noise ratio (SNR) thresholds substantially increase the likelihood of communication failures, especially at distances exceeding 2000 km. For instance, at 3000 km, the outage probability for a 15 dB SNR threshold reaches approximately 25%, compared to less than 5% for a 5 dB threshold. These results underscore the critical trade-offs in designing 5G-satellite IoT networks, balancing bandwidth, frequency, SNR thresholds, and satellite altitudes for optimal performance across diverse IoT applications. The analysis provides valuable insights for enhancing connectivity and reliability in 5G-satellite integrated networks, especially in remote and underserved regions.  
5G networks; satellite systems; IoT applications; two-ray model; signal propagation +
Footnote †: journal: Journal of Selected Topics in Communications
## 4 Power Converter Topologies
### Charger Stage (Bidirectional DC-DC Converters)
#### 4.3.3 Single-Stage Conversion (Z-Source Inverter)  
A single-phase Z-source inverter has been discussed in the literature [52; 53; 55]. The schematic of the Z-Source inverter is shown in Figure 10. One of the main advantages of the Z-converter is the double modulation capability, in addition to the fact that, while regulating the charge of the EV battery, it can simultaneously shape the grid current [53].  
A single-phase modified Z-source inverter (ZSI) topology was proposed by [PERSON] [55]. According to [PERSON] in his proposed topology, the capacitor voltages vary between twice the minimum battery voltage up to twice the fully charged battery voltage when battery voltages vary between 200-500 V. The converter operates between D\({}_{0\text{min}}\) to D\({}_{\text{max}}\) for v\({}_{\text{PVmax}}\) to v\({}_{\text{PVmin}}\) respectively. The proposed cascaded configuration system has the following advantages [55]:  
* Single point of fault is avoided;
* ZSI has a packed structure due to the decreased number of stages;
* Galvanic isolation is feasible between the charger side and the PV and grid;
* Presents an opening to step up the charging power levels by adding such converters;
* Any battery voltage levels can be charged by changing the charger side of the converter between voltage sharing or current sharing;
* Due to the dependency on the size of passive components, higher frequency this inverter can be designed using wide-band-gap devices.