esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Emission-Based Method for IFSS Characterization in Single-Fiber Fragmentation Tests
## 3 Results and Discussion  
### Acoustic Emission Data  
Thirty-five SFFTs were performed to determine the IFSS using optical and AE methods to obtain the length of the fragments. Figure 6 shows the result of the burst detection process applied to the AE signal measured during one of the tests. In Figure 6a, the triangular envelope is shown, which is composed of individual triangles representing the occurrence of AE-bursts. It is important to note that, due to the very short duration of the bursts relative to the total signal length, the triangles in the envelope appear as vertical lines. Figure 6b provides a detailed view of the AE-bursts corresponding to two of the triangles in the envelope. The duration of the tests performed is approximately 20 min, and the time range in which most bursts occur spans around 4 min. Additionally, the average number of AE bursts detected in the tests generally does not exceed 30. This indicates a low temporal density of bursts, meaning that in this type of signal, the conventional fixed threshold method should have no difficulty detecting them. Moreover, the bursts observed in the AE signals (see Figure 6b) exhibit a well-defined shape, which facilitates their detection. The AE waveforms observed in [25; 27] are similar to those obtained in this study.  
Figure 7 shows the triangular envelopes obtained during the burst detection process from AE signals, as well as the (load vs. head displacement) curves obtained during the tests of 16 SFFT samples and the two specimens without fiber. The results indicate the absence of AE burst activity in the samples without fiber, confirming that AE bursts originate from internal failures within the specimen, primarily due to fiber fragmentation. A comparison between the number of AE events and the fragment counts obtained through optical measurements reveals a systematic discrepancy: in most tests, AE detects more fragments than the number identified optically. This apparent overestimation contrasts with the findings of [PERSON] et al. [32], who reported that energy dissipation within the matrix could lead to the loss of AE events. A possible explanation for this discrepancy is the improved sensitivity and resolution of modern AE sensors, which may now be capable of capturing a greater number of localized failure events.  
Figure 6: Results of the triangular envelope burst detection method. (**a**) Triangular envelope of a raw AE signal. (**b**) Detected raw AE bursts.
# Compact 15 mJ Fiber-Solid Hybrid Hundred-Picosecond Laser Source for Laser Ablation on Copper
## 2 Experimental Setup
### Nd:YAG Double-Pass Amplifier  
After being amplified from the regenerative amplifier, the laser pulses pass through a half-wave plate (HWP3), which controls the polarization of the injected pulse to horizontal polarization. HWP3 and TFP5 form an attenuator that can adjust the energy of the injected pulses. Moreover, a spatial optical isolator, including TFP6, HWP4, and FR3, protects the front-stage laser amplifier and blocks the reflected light from the glass substrate of the soft-edge aperture. After exiting this combined isolator, the polarization state of the injected pulses is converted to the vertical polarization. The injected laser beam is expanded and collimated by a pair of plano-concave and plano-convex lenses. The diameter of the expanded laser beam is 4 mm, which is suitable for the aperture of the Nd:YAG crystal rod, avoiding optical damage caused by the high peak intensity during the double-pass amplification process. In addition, a binary amplitude type aperture with softened edge (SA) is inserted to homogenize the laser beam intensity. The SA is set as the object plane of the subsequent spatial filter (SF1). Therefore, the laser beam at the SA is relay-imaged to the center of Nd:YAG through a pair of plano-convex lenses with the same focal length. A pinhole is placed at the confocal point of SF1 to filter out the high-frequency diffraction modulation rings. In our laser source, each spatial filter (SF1, SF2, and SF3) contains a vacuum tube, which is utilized to prevent air breakdown caused by high power density at the confocal point.  
As a function of the millijoule-level energy output of the regenerative amplifier, the laser pulses are further amplified in a Nd:YAG double-pass amplifier. The amplification module (Nd:YAG 2) is a bonded rod-shaped Nd:YAG crystal, consisting of two undoped YAG end caps and a 0.8% doped Nd:YAG gain crystal. The biggest advantage of using the composite rod in the master amplifier is that it can reduce the thermal load on the end surface of the gain medium. The bonded composite YAG-Nd:YAG-YAG possesses 6 mm in diameter and 85 mm in effective pumped length, which is side-pumped by 42 QCW laser bars in a three-direction symmetric geometry. The pumped bars have seven rings, and each ring has six bars. The temperature of circulating water is set at 25 \({}^{\circ}\)C to take away a large amount of heat generated in the gain crystal during high-power pumping. The thermal lens of Nd:YAG 2 is compensated by a plano-concave lens (L1) with a focal length of \(-\)300 mm. Subsequently, another spatial filter (SF2) relays the laser beam at the center of Nd:YAG 2 to the high reflector (HR3). The incident pulses with a vertical polarization state first passes through Nd:YAG 2, L1, SF2, and FR4 in sequence, and then is reflected back by HR3. After passing the FR4 twice, the polarization state of the laser pulses becomes horizontal polarization, and it is finally output through TFP7.
## 5 Experimental Evaluation
### Baseline Schemes  
* Greedy Caching: This approach adopts a greedy approach that considers cache hit rate maximization at each caching level by making caching decisions based on cache miss stream from downstream caches [50]. There are several variations, but the main objective is to replace the cached content with the lowest cache utility. The motivation of the greedy algorithm is to use caching cost to determine which of the same-size contents should be cached. When a cached content is fetched, a value is assigned to it, i.e., the cost of bringing the content to the cache-store. The eviction policy replaces the content with the minimum value and then all the cached contents reduce their assigned values by the minimum value. The time complexity for this implementation is \(O((n-1)\times\log(n))\).
* First In, First Out (FIFO): Without regard for the recency or frequency of accessibility of contents, the FIFO scheme evicts the contents in the order they were added. The first contents added during cache replacement are evicted first. In a FIFO queue, cached contents are placed in the tail of the queue. During cache replacement, cached contents are moved from the head until there is enough space for new contents. The time complexity of the FIFO algorithm is \(O(1)\).
* Time-aware Least Recently Used (TLRU) [51]: TLRU is a variant of LRU and most suitable for information-centric networking (ICN), content delivery networks (CDNs), and distributed networks in general. A time to use (TTU) term is introduced to time-stamp every cached content, which stipulates the usability time for the contents based on the locality of the content and content provider notice. TLRU guarantees that less popular and low lifetime content should be replaced with the incoming content. TLRU has a time complexity of \(O(1)\).
* Least Recently Used (LFRU) [52]: The benefits of LFU and LRU schemes are realized in this combination of caching schemes. It is also suitable for ICN and CDNs. To address the drawback of the Least Frequently Used (LFU) policy where multiple contents are at par on their frequency of file requests, the LFRU is more suitable to distinguish contents on a more elaborate scale, using both frequency and recency of the content requests. Each cacheable content is assigned a Combined Recency and Frequency (CRF) value. The content with the lowest CRF value during cache replacement is evicted. Each file request of a cached content increases its CRF. The time complexity of implementing LFRU ranges from \(O(1)\) to \(O(\log_{2}n)\).
# Enhancing IoT Network Security Using Feature Selection for Intrusion Detection Systems
## 5 Discussion
### Flow SYN Flag Count as an Indicator of Denial-of-Service (DoS) Attacks  
The correlation analysis in this study revealed that 'flow_SYN_flag_count' is strongly associated with denial-of-service (DoS) attacks, particularly SYN flood attacks. This finding underscores the importance of monitoring SYN flag counts as a key metric for detecting anomalous traffic patterns that are indicative of DoS activity. By establishing this correlation, our results validate the relevance of this feature in identifying vulnerabilities in IoT networks.  
DoS attacks, especially TCP SYN flood attacks, are specifically designed to send numerous connection requests to the server, thus overwhelming its resources. One of the critical metrics for identifying and mitigating these attacks is the 'flow_SYN_flag_count', which indicates the presence of abnormal patterns in traffic. Notably, the SYN flag count can be monitored continuously. Vulnerabilities are revealed, based on unusual spikes in activity that deviate from normal traffic patterns in SYN requests. For example, normal and SYN network traffic can easily be differentiated using OpenFlow port statistics and machine learning models, such as random forest classifiers, in software-defined networks. This enables the localization and mitigation of threats in a precise manner [44].  
Moreover, the SYNTROPY framework enhances detection accuracy with Renyi entropy by adjusting its sensitivity to varying network conditions [45]. Enhancing detection accuracy is crucial because attack patterns can be sophisticated and variable in IoT environments. To detect anomalies, such as SYN floods, without updating dynamic data structures, a count-less sketch robustly and accurately executes network measurement tasks in various traffic distributions [46].  
SYN flag monitoring is an important strategy for maintaining service availability under several attack conditions. As high-performance load balancers are frequently the targets of SVN attacks, SYN flag monitoring is one of the innovative schemes that has been proposed to maintain high-throughput connections during attacks [47]. In addition, the flow tables and sketches in the software-defined network can be integrated. This fine-grained traffic measurement enables the separation of mouse and traffic flows and thus contributes to the task of identifying SYN flood attacks [48]. Some approaches, such as tree-based DDoSdetection methods, have early detection capabilities, identifying anomalies in the first few packets. Thus, SYN flag counts can be used to identify vulnerabilities and maintain the security of the network, representing one of the general countermeasures used to protect both traditional networks and IoT environments from DoS attacks.
## 3 Results
### Principal Component Analysis (PCA)  
PCA was used to explain the structure of variation in the collected data on the content of macro- and microminerals in IFs (initial, follow-on, and specialist) and compare it with the structure of variation in breast milk from different lactation periods (colostrum, transitional milk, mature milk). The analysis of variability was conducted among all samples analyzed, and the variables included individual minerals (Ca, Mg, Na, K, P, Fe, Zn, Cu, Mn). PC1 and PC2 explained 63.82% and 11.88% of the total variance in the data, respectively (75.7% overall). Figure 2A shows the relationships occurring between the individual minerals and the resulting principal components. All variables were more highly transferred by PC1. Figure 2B shows a plot of points in the principal component plane, showing the variation in mineral content of the different IFs and individual fractions of breast milk. The distribution of cases relative to each other indicates that there are differences between the mineral composition of IF and HM. The distribution of cases C and TM/MM (breast milk) is clearly separated from all IFs. The formulas S1 and S6 were on the opposite side of the graph in contrast to the other specialist IFs, which indicates the occurrence of differences in the content of individual minerals. Figure 1 indicates that formulas S1 and S6 contained higher levels of Na, K, and Mn relative to the other IFs. The separation of case S5 from the other specialist IFs is a result of the higher Cu content. Figure 2B clearly shows the concentration of cases representing initial and follow-on IFs, indicating the occurrence of a similar mineral profile for these formulas.
## 3 Preliminaries
### Higher Order Cumulants  
First and second order statistics have limitations to handle the non-Gaussian signal distribution, which may reveal some useful information contained in the signal. However, HOS addresses such a limitation by extracting higher-order cumulant information, and computes the unbiased estimate of variables.  
Let us consider a moment generating function \(mg_{X}(i)\) exists for the variable \(X\), whenever the statistical expectation, \(E\big{[}e^{iX}\big{]}\) exists, \(i\in\mathbb{R}\). Therefore, the function \(g_{X}(i)\) can derive the first, second, third and fourth order moments, \(g_{1},g_{2},g_{3},g_{4}\) as  
\[\begin{split} mg_{1}\ x=E\left[X(i)\right]\\ mg_{2}\ x(\tau_{1})=E\left[X(i)\ X(i+\tau_{1})\right]\\ mg_{3}\ x(\tau_{1},\tau_{2})=E\left[X(i)\ X(i+\tau_{1})\ X(i+\tau_{2} )\right]\\ mg_{4}\ x(\tau_{1},\tau_{2},\tau_{3})=E\left[X(i)\ X(i+\tau_{1})\ X(i+ \tau_{2})\ X(i+\tau_{3})\right]\end{split} \tag{2}\]  
Let \(\tau_{1}\), \(\tau_{2}\), \(\tau_{3}\) are the time lag parameters and let \(\{x(\tau)\}\) be a \(n\)th order stationary random process, the \(n\)th order moment is the joint moments of orders up to \(n-1\) which is defined as:  
\[mg_{n}\ x(\tau_{1},\tau_{2},\tau_{3},\ldots,\tau_{n-1})=E[X(i)\ X(i+\tau_{1}) \ X(i+\tau_{2})\ X(i+\tau_{3})\ldots\ X(i+\tau_{n-1})] \tag{3}\]  
The corresponding non-zero mean cumulants \(C_{1}x\), \(C_{2}x\), \(C_{3}x\) and \(C_{4}x\) can be computed as the non-linear combination of the moments as,  
\[\begin{split} C_{1}x=mg_{1}\ x\\ C_{2}x(\tau_{1})=mg_{2}\ x(\tau_{1})\\ C_{3}x(\tau_{1},\tau_{2})=mg_{3}\ x(\tau_{1},\tau_{2})\\ C_{4}x\left(\tau_{1},\tau_{2},\tau_{3}\right)=mg_{4}\ x(\tau_{1},\tau_{2}, \tau_{3})-mg_{2}\ x(\tau_{1})\ m_{2}\ x(\tau_{2}-\tau_{3})-mg_{2}\ x(\tau_{2}) \ m_{2}\ x(\tau_{3}-\tau_{1})-mg_{2}\ x(\tau_{3})\ m_{2}\ x(\tau_{1}-\tau_{2}),\end{split} \tag{4}\]  
and the \(n\)th order cumulant is derived using the \(n\)th order moment as:  
\[C_{n}x(\tau_{1},\tau_{2},\tau_{3},\cdots,\tau_{n-1})=mg_{n}\ x(\tau_{1},\tau_{ 2},\tau_{3},\cdots,\tau_{n-1})\ -mg_{GN}\ x(\tau_{1},\tau_{2},\tau_{3},\cdots,\tau_{n-1}), \tag{5}\]  
where, \(mg_{n}\ x(\tau_{1},\tau_{2},\tau_{3},\cdots,\tau_{n-1})\) is the \(n\)th order moment and \(mg_{GN}x(\tau_{1},\tau_{2},\tau_{3},\cdots,\tau_{n-1})\) is the \(n\)th order moment of the corresponding Gaussian process. The first order cumulant is the mean of the process while the second order cumulant is the autocovariance sequence, and the third is designed as the third central moment. The zero lag cumulants have special names: \(C_{2}x(0)\) is the variance, \(C_{3}x(0,\,0)\) is the skewness and \(C_{4}x(0,\,0,0)\) is the kurtosis. Third-order cumulants are preferred to extract the features from the raw CSI signal without applying any pre-processing. Fourth order cumulants build highly complex matrices, need huge computational efforts, and do not justify the recognition accuracy benefit over the efforts involved. Hence it is not considered in the present work.
## 3 Results
### Final Filter Design
#### 3.2.2 Experimental Results  
The results collected from the second iteration devices are shown in Table 5; measurements were carried out on replicas of the same optimized filter structures, fabricated on different chips, and the minimum and maximum values obtained at the central resonance (the one closest to 1550 nm) are reported. The experimental results obtained using the newly-designed filter, showed some significant differences and improvements with respect to those of the first iteration in Table 3. In particular, a significant reduction of the insertion loss was observed for the ring-based filter, thanks to the increased radius. An example of the OSA-acquired spectra of the drop and through outputs, for the best performing ring filter, is shown in Figure 5. The performance of the racetrack-based device slightly deteriorated, because of the higher round-trip losses, introduced by the short-radius curves, and because of the change in the modal index between the curved and the straight sections. The variation in the measured absolute resonance wavelength for nominally identical filters, was found to be <0.4 nm for structures fabricated on the same chip, and <4 nm for structures with the same design across different chips.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 4 Q-Learning-Based Position-Aware Routing Protocols in FANETs
### QL-Based Multi-Objective Fuzzy Routing Protocol (QLMF)  
[PERSON] et al. [53] proposed the QL-based multi-objective fuzzy routing (QLMF) for FANETs, in which the next hop is selected by sending the link and path-related parameters to a fuzzy controller. The link-related parameters are the transmission rate, energy state, and flight status, such as similarity in the flying direction. The path-related parameters are dynamically updated by the RL, which maintains two different Q-values: hop count and successful packet delivery time. Therefore, the fuzzy controller uses three link-related parameters and two path-related parameters as the input membership functions. Subsequently, the parameter is divided into two-level fuzzy linguistic variables that are inferred with the predefined rule base to select a better forwarding node based on the output crisp value after defuzzification.  
_Advantages:_ QLMF outperforms Q-value-based ad hoc on-demand distance vector routing in terms of hop count and energy consumption.  
_Limitations:_ Maintaining two different Q-values for each UAV is challenging, and the learning rates are fixed. The delay is calculated considering the number of hops required to reach the destination, which is not appropriate for MAC, queuing, and transmission delays, and must be calculated for precise estimation.
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 2 Background and Related Work
### Consensus Protocols for Blockchain
#### 2.2.1 Nakamoto Protocols  
The Nakamoto protocol is implemented as the consensus foundation of Bitcoin [7], and it is widely adopted by many cryptocurrency-based blockchain networks such as Ethereum [17]. The Nakamoto protocol adopts a computation-intensive PoW, which requires all participants to compete for rewards through a cryptographic block-hash value discovery racing game. The PoW consensus demonstrates security and scalability in an asynchronous open-access network as long as an adversary does not control the majority (51%) of the miners. However, the brute-force PoW mining process also incurs a high demand in terms of computation and energy consumption such that it is not affordable on resource-constrained IoT devices.  
In order to improve performance and resource usage efficiency in PoW, a number of alternative Proof of X-concept (PoX) schemes have been proposed. Permacoin [8] repurposes mining resources in PoW to achieve distributed storage of archival data. The Permacoin adopts PORs [18], which require miners to present random access to a copy of a file from local storage as valid proof for successfully minting money. Permacoin requires participants to invest in its storage capacity rather than solo computational power. It could reduce unnecessary wastage of computational resources in PoW and mitigate centralized mining pools issue.  
Similar to Permacoin, a Resource-Efficient Mining (REM) [19] scheme is proposed to achieve security and resource efficiency based on the partially decentralized trust models inherent in Intel Software Guard Extensions (SGX). The REM utilizes a Proof-of-Useful-Work (PoUW) consensus protocol, which requires miners to provide trustworthy measurements on CPU cycles used by its useful workloads in SGX-protected enclave. Compared with Proof-of-Elapsed-Time (PoET) in Sawtooth [20] that uses random idle CPU time as proofs, PoUW in REM not only prevents the stale chip problem but also yields the smallest amount of mining waste.  
In order to reduce energy consumption caused by intensive hash value calculating in PoW, Peercoin [21] adopts Proof-of-Stake (PoS), which leverages the distribution of token ownership to simulate a verifiable random function to propose new blocks. Such a process of efficient \"virtual mining\" manner allows PoS miners to only consume limited computational resources in order to generate new blocks. Similarly to PoW, PoS guarantees security as long as an adversary owns no more than half of the total stakes in the network.  
Unlike PoW and its variants, the PoENF consensus scheme neither requires high demand of computation and storage for mining nor depends on security guarantees supported by trusted hardware or monetary deposit stake. It is suitable for heterogeneous IoVT devices connected to the power grid.
# Vulnerability of Buildings to Meteorological Hazards:
## 2 Materials and Methods
### The Impact of Meteorological Hazards on Buildings
#### 2.1.3 The Impact of Hail on Buildings  
According to the WMO [24], hail is \"precipitation in the form of particles of ice (hailstones)\". Despite the frequent occurrence of the process, there is still a strong need to systematically collect information on hail damage [25]. The degree of damage to buildings clearly depends on the particle size. According to Munich Re [26], damage to greenhouses may occur with a hail diameter of 2 to 3 cm, whereas light roofs may be damaged by hail with a diameter of 3 to 4 cm. Windows may break after the impact of larger hail particles (diameter of 4 to 6 cm). Significant building damage, including damage to facades and window frames, has been recorded for hail as large as a tennis ball (6 to 8 cm diameter). Finally, hail with a diameter that exceeds 11 cm may be responsible for damage to the building structure. It is therefore clear that analysis of the physical vulnerability of buildings to hail has to include information regarding the roofs, windows, and building material.