esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Analysis
### Material Properties  
In the collision analysis, the material properties were applied using the revised equations reported in reference [PERSON] et al. [7]. These equations were developed using the rigorous dynamic tensile test results on different steels. Equations (2)-(6) were applied to consider the yield plateau and strain hardening. The effect of strain-rate hardening was also included by using Equations (7)-(10). In this paper, the range of strain rates was performed with 10/s, 20/s, 50/s, 70/s, 100/s, to 150/s, as indicated in Figure 2.  
\[\sigma_{tr}=E\varepsilon_{tr}\text{ when }0<\varepsilon_{tr}\;\leq\;\varepsilon_{Y,tr} \tag{2}\]  
\[\sigma_{tr}=\sigma_{Y,tr}+(\sigma_{HS,tr}-\sigma_{Y,tr})\frac{\varepsilon_{tr} -\varepsilon_{Y,tr}}{\varepsilon_{HS,tr}-\varepsilon_{Y,tr}}\text{ when } \varepsilon_{Y,tr}<\varepsilon_{tr}\;\leq\;\varepsilon_{HS,tr} \tag{3}\]  
\[\sigma_{tr}=\sigma_{HS,tr}+K(\varepsilon_{tr}-\varepsilon_{HS,tr})^{n}\text{ when } \varepsilon_{HS,tr}<\varepsilon_{tr} \tag{4}\]  
where  
\[n=\frac{\sigma_{T,tr}}{\sigma_{T,tr}-\sigma_{HS,tr}}(\varepsilon_{T,tr}- \varepsilon_{HS,tr}) \tag{5}\]  
\[K=\frac{\sigma_{T,tr}-\sigma_{HS,tr}}{\left(\varepsilon_{T,tr}-\varepsilon_{ HS,tr}\right)^{n}} \tag{6}\]  
Figure 1: Finite element modelling of the stringer-stiffened cylinder.  
\[\frac{\sigma_{YD}}{\sigma_{Y}}=1+0.3(E/1000\sigma_{Y})^{0.5}\big{(}\dot{\epsilon} \big{)}^{0.25} \tag{7}\]  
\[\frac{\sigma_{TD}}{\sigma_{YD}}=1+\Big{[}0.16(\sigma_{T}/\sigma_{YD})^{3.325} \big{(}\dot{\epsilon}\big{)}^{1/15}\Big{]}^{0.35} \tag{8}\]  
\[\frac{\varepsilon_{HSD}}{\varepsilon_{HSS}}=1+0.1(E/1000\sigma_{Y})^{1.73} \big{(}\dot{\epsilon}\big{)}^{0.33} \tag{9}\]  
\[\frac{\varepsilon_{TD}}{\varepsilon_{T}}=1-0.117\Big{[}(E/1000\sigma_{T})^{2. 352}(\sigma_{T}/\sigma_{Y})^{0.588}\Big{]}\big{(}\dot{\epsilon}\big{)}^{0.2} \tag{10}\]  
where  
\(\sigma_{tr}\), \(\varepsilon_{tr}\): true stress and strain, respectively;  
\(\sigma_{Y,tr}\), \(\sigma_{HS,tr}\), \(\sigma_{T,tr}\): true yield strength, true hardening start stress, and true ultimate tensile strength, respectively;  
\(\varepsilon_{HS,tr}\), \(\varepsilon_{T,tr}\): true hardening start strain and true ultimate tensile strain, respectively;  
\(\sigma_{TD}\), \(\sigma_{YD}\): dynamic ultimate tensile strength and dynamic yield strength, respectively;  
\(\varepsilon_{T}\), \(\varepsilon_{TD}\): ultimate tensile strain and dynamic ultimate tensile strain, respectively;  
\(\varepsilon_{HSD}\), \(\varepsilon_{HSS}\): dynamic hardening start strain and static hardening start strain, respectively; and  
\(\varepsilon_{Y}\), \(\dot{\epsilon}\): yield strain and equivalent strain rate, respectively.
## 8 Performance Analysis
### Storage Overhead  
We first define that identity, hash, timestamp, random number, and public key cryptosystem are 20, 20, 4, 20, and 40 bytes, respectively. In our protocol, stored messages \(\{A_{i},B_{i},C_{i}\}\) and \(\{RID_{i},RID_{j},X_{i}\}\) require (\(20+20+20=\)) 60 and (\(20+20+20=\)) 60 bytes, respectively. Although the proposed scheme storage overhead of somewhat higher than [PERSON] et al.'s scheme, it provides better efficiency and security than the other related schemes [6,20,21,28]. Table 8 shows the analysis result of storage overhead compared to existing schemes.
## 4 Discussion
### Limitation and Future Research  
There are some limitations to our study. First of all, the experimental materials selected in this study are abstract or concrete graphics and Chinese characters, and the research results can only serve as the terminal interface of Chinese applications. In future research, this should be expanded to other languages and combination icons to better serve the visual cognition of graphic and text combination icons by users with different cultural backgrounds. Secondly, construal level personality traits are the implicit and stable cognitive traits of users. How to quickly identify user types and accurately present a usable combination icon during the operation of the system remains a challenge. It is an urgent problem that needs to be solved in the future in order to combine advanced technologies, such as artificial intelligence, in actual program development. Thirdly, the design styles of icons have diversified, in terms of line shape, surface shape, flattening, and 3D or graphic-text combined icons. In addition, our experimental data assume that users are relatively equal in terms of their familiarity. When two types of icons have different levels of familiarity, and especially when users with low construal levels are familiar with abstract combination icons, in this way, their visual cognition can increase significantly. In this case, how will the cognitive performances of users with different construal levels change? The combined external design features and users' internal cognitive characteristics will interact with each other, and so more research and exploration are needed. Furthermore, due to the limitation of the size of the cohort, this study is limited to the student designer group, and a larger socially oriented sample size may be required to test our findings in the future. All in all, the results of this study may be more applicable to system program development for user groups with different construal levels of Chinese traits.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 6 Discussion and Open Research Challenges
### From the Network Protocols Perspective  
When existing network protocols are evaluated in the context of nanosatellites technology for IoT connectivity, it is common to encounter difficulties in finding one protocol that meets all the requirements. In the case of MAC protocols, Figure 14 shows how an ideal performance zone, derived from the IoT scenario together with the CubeSat restrictions, is not yet met by any of the reviewed protocols, even though many had good performance in more traditional satellite scenarios. Additional research for MAC protocol design is needed to integrate aspects which can operate with low processing capacity demands and adapt to a variable and dynamic number of ground sensors and devices. Moreover, mitigation mechanisms should be considered for managing the high power imbalance conditions over the network links, the unequal delays derived from uneven link lengths, and the inability to provide high-quality channel estimations.
# Five-Axis Finish Milling Machining for an Inconel 718 Alloy Monolithic Blisk
## 3 Finish Machining Strategy and Method
### Cutting Speed  
#### 3.1.1 Processing Conditions and Parameters  
It was reported that feed has no significant effect on carbide cutter milling Inconel 718 [17]. Nevertheless, cutting speed \(V_{c}\) is related closely to cutting force and tool wear. An appropriate range of cutting speed for side milling has been proposed in the literature. Hence, in our experiments, the depth of cut \(A_{p}\) and feed per tooth \(F_{z}\) were fixed, and different cutting speeds were set (ranging from 26 to 96 m/min). The effective cutting speed \(V_{c}\) during the cutting process is very important. It is calculated based on the effective diameter \(D_{c}\), as shown in Figure 7[21].  
\[D_{c} =D_{c}\sin(\varphi+\theta) \tag{1}\] \[\cos\theta =1-\frac{2A_{p}}{D_{c}} \tag{2}\]  
Figure 6: The machining process: (**a**) Blank, (**b**) Roughing, (**c**) Semi-Finishing, (**d**) Finishing.  
where \(D_{c}\) is the ball nose diameter. \(\varphi\) is the inclination angle of the tool. Because the lead angle is set to \(-30^{\circ}\) in the NX program, the maximum tool inclination angle is \(30^{\circ}\). The maximum effective diameter \(D_{e}\) can be calculated to be 2.115 mm. When the cutting depth is tiny, the effective cutting speed is much lower than the setting speed, and the tool cutting conditions will be lower than its application range. Therefore, this study recalculated the effective cutting speed \(V_{e}\).  
\[V_{e} =V_{c}\frac{D_{e}}{D_{c}} \tag{3}\]  
Thus, eight experiments were performed. The effective cutting speeds after recalculation are listed in Table 5. Each experiment was a complete process in which a new tool was used for blade cutting. In Experiment 7, the cutting speed was originally set to 86 m/min, and the spindle speed was set to 9125 RPM, which resonated with the machine. Therefore, the cutting speed was increased to 91 m/min in this experiment. Under the fixed feed per tooth, an increase in the cutting speed also engendered an increase in feed, which reduced the time required for processing.
# A Taxonomy for Threat Actors' Delivery Techniques
## 3 Problem Statement  
In the modeling of offensive operations, the establishment of an accurate sequence of actions and the identification of the tactics and techniques that threat actors perform in order to achieve their goals is a key requirement for the prevention, detection, and neutralization of the threat. The analysis of the initial foothold into a target infrastructure is a must for this modeling of hostile activities. The Cyber Kill Chain \"Delivery\" stage or the MITRE ATT&CK \"Initial access\" tactic both represent this point of initial contact between the threat actor and its target.  
The delivery tactic is usually linked to the delivery of a malicious payload to the target, embedded into a weaponized such as a particular file type or web link. In fact, in many references delivery is just identified as \"payload delivery\" [18; 19; 20] or \"malware delivery\" [21; 22]. The delivered has been generated on a previous stage of the operation, usually called weaponization [7], and, after a successful delivery, the payload is detonated, starting the next stage of the attack, which is commonly identified as exploitation.  
This focus on the delivery of malware or malicious payloads has relevant limitations. It refers only to specific delivery techniques, while other ones, whose use is increasing on a daily basis, are not considered in this concept. Malwareless operations do not use malicious payloads, not only to achieve the delivery tactic, but none of the required tactics perform a successful operation.  
Nowadays, threat actors' delivery techniques include not only the weaponization of a malicious payload, but also different approaches, some of them even without a malicious payload, that allow hostile actors to accomplish their goal: to break the target's security perimeter and to open the way for the exploitation stage.  
We have analyzed different approaches for the identification of a suitable up-to-date classification scheme for delivery techniques, especially for the ones not linked to the use of malicious code and simply being the abuse of legit resources. In Section 4 we present a summary of these approaches. However, no suitable approximation for such a structure that allows analysts to detect an ongoing operation has been identified. It is mandatory to analyze, understand, and identify the different techniques for delivery used nowadays, in order to be able to detect and neutralize them. Such a structure would allow analysts to identify gaps in their security countermeasures and to discover new techniques deployed for the delivery tactic.
## 2 Related Work  
Energy-efficient MAC development for WBAN is a very prominent field of research and is receiving increasing attention. In a WBAN survey [9] we have found that amongst the well-known protocols, IEEE 802.15.4 standard is one of the earliest MAC proposals for WBANs. In this protocol, a beacon-enable or non-beacon enable mode decides how to execute the medium access operation. In beacon-enable operations, a central node (_i.e._, gateway) serves as the coordinator for time-synchronization and superframe management. The superframe structure consists of an initial beacon, a contention access period (CAP) for CSMA/CA, a contention free period (CFP) for TDMA and an inactive period (IP) for sleep. However, the fixed CAP and CFP of IEEE 802.15.4 limit adaptive operation with fluctuations in the traffic load. During low traffic periods the fixed CAP and CFP result in higher energy consumption and low capacity utilization, respectively. Conversely, in a recent analysis [21] it is found that the non-beacon enable mode of IEEE 802.15.4 is suitable for prolonging the life time of a network with very low traffic (_i.e._, periodic observation data). However, in reality consideration for only low-rate data transmission in WBAN is a very limited and infeasible assumption.  
A number of studies in the literature extend the concept of the IEEE 802.15.4 protocol [9]. The LDTA-MAC [3] is one such extension that enables the transmission of emergency traffic over the network. However, the protocol does not address the dynamic and diverse characteristics of the traffic load and lacks in traffic adaptability. We have expounded on the shortcomings of LDTA-MAC in Section 3. The scheme proposed in [16] is an energy efficient pure TDMA protocol that is developed mainly for handling a large amount of data. However, this protocol is implemented with a fixed topology system and suffers from low capacity utilization when low-rate traffic exists in the network. A battery-aware TDMA protocol is proposed in [18] considering the node energy discharge, dynamic wireless channel and node buffer model characteristics. Although this protocol is able to extend the network life-time, it cannot prevent congestion losses due to buffer overflow. Recently, a heart beat driven H-MAC protocol [17] is proposed for WBANs in which each sensor must be synchronized with the human heart beat rhythm. However, the synchronization and its maintenance in H-MAC are very complex. There are a few more WBAN MAC protocols such as reservation-based dynamic TDMA (DTDMA) [22] and BodyMAC [23].  
Review of the literature clearly indicates the need for a traffic load-aware WBAN MAC protocol that would work energy efficiently and can meet high-throughput demand in a dynamic traffic environment. To the best of our knowledge, there is not yet such a scheme for WBAN and we believe our proposed ATLAS protocol would meet these needs.
## 5 Conclusions
In summary, the adoption of measurement devices and technologies able to guarantee an accuracy of no more than 20 mm, a frequency no less than 100 Hz, and a latency in data transmission no more than 10 ms, is suggested at the end. Conversely, the structural response risks assuming peak values of double this, with respect to the target established in the calibration of the control algorithm. It is worth underlying that the simulations were conducted with reference to a limited number of load cases, and a single type of wind turbine and control algorithm, therefore the outcomes cannot be generalized without reservations. Nevertheless, they provide useful information for making preliminary decisions when designing a semi-active control system to be integrated with the necessary monitoring system. The authors intend to carry out future field trials to validate the results of this numerical research as well as comparative evaluations of the various technologies, considering the costs involved. Once the activities, with reference to the single turbine, are completed, the authors believe it is important to extend this research work to a wind farm made up of dozens of towers. This also combining the monitoring of absolute displacements of individual towers with that of relative displacements between adjacent towers. N.C. and G.P. contributed for conceptualization, methodology, writing original draft, supervision, project administration and funding acquisition. M.S. and U.R. contributed for data curation, software, visualization, writing original draft, review and editing. All authors have read and agreed to the published version of the manuscript. The research activity has been supported by the University of Naples 'Parthenope' with a 2017 grant within the call 'Support for Individual Research for the 2015-17 Period' issued by Rectoral Decree No. 289/2017. The abovementioned support is acknowledged. The authors declare no conflict of interest.
# Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques
## 2 Materials and Methods
### Hyperspectral Scanner  
The hyperspectral images of all samples were acquired by means of a hyperspectral scanner (DV Optic s.r.l., Padova, Italy), equipped with two cameras with corresponding spectrographs capable of acquiring reflectances in the visible and near-infrared range. The visible sensor acquires reflectance data in a wavelength range between 400 and 1000 nm, and the near-infrared sensor acquires between 900 and 1700 nm. The instrument setup for this thesis work comprises a moving-plane hyperspectral scanner (DV-Optic s.r.l.) equipped with two distinct types of sensors.  
The primary sensor is a progressive scan camera (AVT F100 B, Allied Vision Technologies, Stadtroda, Germany), which incorporates a 16-bit charge-coupled device (CCD) chip (Kodak KSI 1020, Stuttgart, Germany). This camera is coupled with an ImSpector V10 spectrograph (Specim Ltd., Oulu, Finland) that operates within a wavelength range of 400 to 1000 nm, providing a sampling rate of 5 nm. The spectrograph employs prismatic reticular optics and is fitted to an objective with a C f 1\(A\) attack. Its function is to disperse the incoming radiation along the scan line, generating spectral information for each spatial position.  
The secondary sensor is a short wave infrared (SWIR) camera, featuring an InGaAs sensor (Xeva InGaAs VIS/NIR 320 \(\times\) 256 FPA, Xenics nv, Leuven, Belgium). This camera is capable of acquiring reflectances at wavelengths from 1000 to 1700 nm, also with a sampling rate of 5 nm. As for the NIR spectrograph (covering 900-1700 nm), the detector utilized is of the complementary metal-oxide semiconductor (CMOS) type. The illumination sources are different for the two types of sensors.  
For the VIS/NIR sensor, a 150 W halogen illuminant was employed. This illuminant was connected to a fiber optic guide, which terminated in a linear collimator designed to illuminate the scanner's line of sight. Conversely, for the SWIR sensor, a distinct halogen source (EKE 9596 ER, 150 W) was utilized, characterized by an emission spectrum ranging from 800 to 2000 nm. This illuminant was also equipped with a fiber optic guide and a linear collimator. To facilitate the sensors' reception of reflected radiation from the object, two mirrors, positioned at a 45\({}^{\circ}\) angle, were strategically placed within the line of sight of each sensor.
## 3 Results and Discussions  
### Experimental Results  
This section discusses the effect of varying key parameters on the specimen's response using the spring-driven dynamic impact testing setup. The tensile spring extensions were incrementally adjusted for the eight steel specimens, as summarized in Table 5. As seen in Figure 6, increasing the spring tension increased impact velocity, force, and energy, along with the measured response of the impacted specimen.  
The dynamic impact force recorded at the mid-span of the steel specimens across various impact velocities is shown in Figure 7. The results indicate that higher impact velocities increase the impact force while reducing the duration of loading and strain rate. Displacement-time histories for the specimens, depicted in Figure 8, confirm that most specimens remained in the elastic zone, except for the specimen tested at 0.65 m/s. The transition from elastic to plastic behavior in this specimen is evident from the straight line in Figure 9, attributed to plastic strain and residual deformation.  
The load-displacement behavior of the steel plate from both quasi-static and dynamic tests is presented in Figure 8. As the impact velocity increases, the difference between the dynamic impact load and the equivalent static load widens, highlighting the impact velocity's influence on the material's response. The developed testing device effectively captured the specimen's response under varying impact velocities.  
Figure 5: The proposed simulation model of the dynamic three-point bending testing setup: (**a**) 3-point testing apparatus attached to the setup; (**b**) implemented finite element model; and (**c**) applied boundary conditions.  
Figure 8: Displacement–time history for the dynamic impact testing.  
Figure 6: Tuning the tensile springs in the setup to generate (**a**) the impact velocity and energy for a given (**b**) load and displacement on the specimen.  
Figure 7: Impact force–time history for the dynamic impact testing.  
To ensure accuracy, an uncertainty analysis was conducted. Sources of uncertainty included material property variations, velocity and force measurement errors, and spring calibration. The combined standard uncertainty was calculated using the root-sum-square method, resulting in \(\pm\)5% uncertainty for displacement measurements and \(\pm\)7% for force measurements.
# Research and Application of Semi-Supervised Category Dictionary Model Based on Transfer Learning
## 3 Experiments and Results Analysis
### Analysis Object
#### 3.1.2 Data of Bearing Fault Diagnosis  
For the second experiment, the experimental data employed in this study were procured from the Bearing Data Center at Case Western Reserve University. As depicted in Figure 3, the bearing failure experimental platform comprises induction motors, torque sensors, linkage devices, and electronic controllers, among other components [23]. An acceleration sensor, attached to the housing, is used to capture vibration data at a sampling frequency of 12 kHz. The fan load can be adjusted to different horsepower (hp). Faults manifest in the inner ring, outer ring, and the rolling body, each with varying levels of degradation depth. Figure 4 presents the waveform of the vibration signal captured in the inner ring failure mode in the dataset, demonstrating irregular fluctuations. Due to the complex operational environment of rolling bearings, the data not only encapsulate intrinsic fault information, but also capture the vibration information generated by other rotating mechanical parts, along with their noise.  
The bearing failure dataset from Case Western Reserve University was utilized for this study, where load magnitude served as a control parameter for constructing the source and target domain datasets. Data under no-load conditions, encompassing normal and fault states, were designated as the source domain. This domain includes four categories: normal, inner ring fault, rolling element fault, and outer ring fault, each featuring multiple degradation depths. Addressing the issue of ultra-long vibration data in fault diagnosis, the original data were segmented into multiple data blocks based on a specified length. In the conducted experiment, the sample length was set to 1100. To mitigate boundary effects caused by truncation, and ensuring an overlap between different data blocks [24],  
Figure 3: Bearing fault diagnosis platform structure schematic diagram.  
the overlap length was set to 60. Prior to these operations, the sample data underwent normalization.  
In order to facilitate a comprehensive comparison of the performance of various models, mean, standard deviation, and maximum values were computed for the bearing fault diagnosis task. These measures, obtained by conducting 10 repetitions, served as the performance indices. Under no-load conditions, 200 samples from each category, amounting to a total of 800 labeled samples, were curated to generate the source domain dataset. Under a 3 horsepower load condition, 10 samples from each category, summing up to 40 labeled samples, were selected to constitute a small number of labeled samples in the semi-supervised target domain. The remaining 1477 samples were utilized as the test set in the target domain.