esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Identification of Signal App Traffic
#### 4.2.2 Target Device (User A) Typing Pattern  
To notify the patterns of the Signal app when target User A starts typing a message in a chat window, certain flow patterns with fixed payload sizes were noticed. To be assured, these patterns were observed several times in trace files to deduce results as shown in Figure 7. Target User A is highlighted with IP 192.168.137.69. It was noticed that when User A with IP 192.168.137.69 started typing in a chat window, 1181, 1182 and 1183 bytes of data packets with 1115 and 1116 payload size were sent to the Signal server. In response, the server sent 139 or 140 bytes of data packets with payload sizes of 73, 74 to the Signal client. The Signal client in response sent an empty packet of 66 bytes with payload size 0 to the server as an acknowledgment of the previous packets. Meanwhile, it was noticed that the Signal app indicated the typing status to the respective Signal servers, even though the message was not being sent to another party.  
Figure 5: The authentication process of Signal app for connection establishment.  
Figure 6: Opening/accessing the Signal app.
## 3 Numerical Simulation Results and Analysis
### Failure Characteristics of the Double U-Shaped Caverns under Vertically Incident Stress Wave
When the cavern clearance decreases to 1.25 m, the density of microcracks in the intermediate rock pillar increases significantly, the failure surfaces on both sides interconnect, and the rock pillar undergoes overall instability failure. Taking the midpoint of the right sidewall of cavern 1 (C1) as an example, the evolution process of the kinetic energy of the intermediate rock pillar under vertically incident stress waves is illustrated in Figure 10. When the cavern clearances are 2.5 and 3.75 m, the kinetic energy at point C1 increases first and then decreases over time. When the kinetic energy descends to a certain level, corresponding failure occurs. Due to the static stress of the rock, a certain amount of strain energy is accumulated in the intermediate pillar. As the failure occurs, the release of stored strain energy induces an increase in kinetic energy at C1, reaching a steady state ultimately. When the cavern clearance is 3.75 m, the residual kinetic energy during the failure process mainly derives from the external dynamic disturbances, with a relatively smaller proportion from the release of stored strain energy. With decreasing cavern clearance, the accumulated strain energy in the intermediate rock pillar increases, and the proportion of released strain energy in residual kinetic energy rises. Particularly, when the cavern clearance is 1.25 m, the residual kinetic energy at the periphery of the rock pillar (C1) far exceeds the energy carried by the incident stress wave, and the released strain energy induces the violent rockburst in the intermediate rock pillar. Figure 9: Failure modes of surrounding rock under vertically incident stress wave (\(P_{d}\) = 60 MPa): (**a**) \(d\) = 3.75 m; (**b**) \(d\) = 2.50 m; (**c**) \(d\) = 1.25 m.  
Since the incident direction of the stress wave is consistent with the maximum principal stress direction, the number of microcracks of the model under the vertical incident stress wave is higher than that under the horizontal incident stress wave. The arch-shaped surfaces both formed on the intermediate rock pillar under different incident directions. However, when the stress wave is incident horizontally, the failure around the periphery of the intermediate pillar is caused by the reflected tensile stress wave, and the failure process is relatively gentle, accompanied by less residual kinetic energy. This phenomenon is similar to the pillar failure observed by [PERSON] et al. [36]. As shown in Figure 11a, an hourglass-shaped pillar formed in a stone mine of the Eastern and Midwestern United States due to spalling. When the stress wave is vertically incident, the failure process around the intermediate pillar becomes more severe, and the rockburst can be observed, especially for the small cavern clearance. A similar failure phenomenon was recorded by [PERSON] and [PERSON] [37]. They observed the occurrence of pillar burst induced by earthquake, accompanied by the ejection of a large number of rock blocks (Figure 11b).
# A Blockchain Protocol for Authenticating Space Communications between Satellites Constellations
###### Abstract
Blockchain over satellite removes the dependence on terrestrial networks for the storage, broadcasting, or processing of space data, hence eliminating significant vulnerabilities for a data breach or distortion. testing, and launching. In the future, the blockchain-satellite system will depend on a cloud constellation for managing data centers in orbits where companies can upload their data and bypass the ground networks; this will help the governments and companies to get information from different sources and orbits in space [20]. * _Designing Open source Satellite_: Large space companies started to develop blockchain-based open-source satellite networks for providing many services to end-users on the ground and enabling them to directly access satellite services. For instance, Singapore-based Space Chain [21] has begun to build the world's first open-source Blockchain-based satellite network. Space Chain permits end users to develop and run decentralized applications by accessing the open-source satellites in space.
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 4 Experiment
### Ablation Study and Analysis
#### 4.4.1 Effectiveness of STPro  
ST refers to the method employing STPro, while Baseline still indicates that our model solely activates a branch for global feature learning without the SFM. Add and Multiply represent different ways of fusing scaled visual feature distance and scaled STPro. According to Table 3, methods integrated with STPro significantly outperformed those only using visual features, which demonstrates the effectiveness of mining spatial-temporal patterns. When visual appearance features alone prove insufficient for determining a vehicle's identity, its spatial-temporal cues provide complementary information, thus enhancing vehicle re-identification. Additionally, it is evident that the fusion of visual feature similarity and STPro performed better in a multiplicative manner compared to an additive way. This supports our intuition that the visual features of vehicle images and their spatial-temporal cues are highly independent, hence treating their fusion as a joint probabilistic processing of independent events is justified.
## Abbreviations  
ABTS: 2,2\({}^{\prime}\)-azino-bis-(3-ethyl-benzothazoline-6-sulfonic acid); AO: acridine orange; AR: acid red; ARA: \(\alpha\)-L-arabinofuranosidase; BDC: 1,4-benzenedicarboxylic acid; Bglhi: \(\beta\)-glucosidase from _Humicola insolens_; CB: chitosan beads; CLEAs: crosslinked enzyme aggregates; CMS: chitosan-based minispheres; CNTs: carbon nanotubes; CR: Congo red; CS: chitosan; CSBs: core-shell CS beads; DA: dopamine; DD: deacetylation degree; DEO: 1,2,7,8-diepoxyoctane; DS: diclofenac sodium; DVB: divinylbenzene; ECH: epichlorohydrin; EDAC: 1-ethyl-3-[3-dimethylaminopropyl]carbodiimide; Egst: endoglucanase from _Scytalidium thermophilus_; FA: formaldehyde; 6G: \(\beta\)-D-glucosidase; GA: glutaraldehyde; GNPs: graphene nanoplates; GO: graphene oxide; GOx: glucose oxidase; GP: genipin; GTMA: glycidyl trimethylammonium chloride; HA: hyaluronic acid; HRP:horseradish peroxidase; HSDH: hydroxysteroid dehydrogenase; IC: indigo carmine; INU: inlusocarse; LAC: lacase; LIP: lipase; MAH: maleic anhydride; MB: methylene blue; MCs: microcrystals; MG: malachite green; MOFs: metal-organic frameworks; MPs: microparticles; NA: nitroaniline; 4-NBA: 4-nitrobenzoic acid; NHS: N-hydroxysuccinimide; NIL: amino-functionalized ionic liquid; nM: nanomagmetine; NPs: nanoparticles; 2(34)-NP., 2(3,4)-nitropenol; OA: oleic acid; OD: oxidized dextran; PAA: poly(acrylic acid); PAHs: polycyclic aromatic hydrocarbons; PANI: polyaniline; PDA: polydopamine; PE: polyelectrolyte; PEI: polyethylenimine; PFCs: perfluorinated chemicals; PFOA: perfluorooctanoate; PFOS: perfluorooctane sulfonate; PMA: poly(methyl acrylate); PIPMB: 3,3-diphenylpropylimine methyl benzaldehyde;RR: reactive red;SDBS: sodium dodecyl sulfate; SP: spermine; SPase: sucrose phosphorylase; TCS: thiolated CS; TPP: tripolyphosphate; ZF: zinc ferrite.
# Deep Learning-Enhanced Spectrogram Analysis for
## 3 Methodology
### Dataset and Preprocessing
Larger resolutions increased the training time without significant accuracy improvements, while smaller resolutions led to information loss and reduced classification performance. The 128 \(\times\) 128 size offered a practical balance between computational efficiency and sufficient resolution for learning spatial and frequency-dependent patterns in the spectrograms. Recent studies, such as [PERSON] et al. [32], have demonstrated that the choice of data augmentation techniques significantly affects spectrogram-based CNN classification performance. Methods such as horizontal flipping and PCA-based transformations have been shown to improve generalization, whereas others, such as noise injection, may introduce misleading spectral patterns. Figure 2 presents a summary of the proposed classification pipeline.
## 3 Synchronization and Channel Estimation for TIBWB-OFDM  
In order to perform signal synchronization and channel estimation tasks for TIBWB-OFDM, both transmitter and receiver schemes presented in [6] need to be improved for practical deployment. The two following subsections explain the proposed transceiver architecture in more detail.  
The inherent characteristics of the TIBWB-OFDM scheme favor the adoption of a preamble allocation strategy due to the packing of several OFDM symbols into a single block. So, as shown in Figure 1, the interleaved block vector is attached to the \(N_{p}\)-length preamble sequence \(S_{Z\mathcal{C}}\), forming a new TIBWB-OFDM frame block.  
Due to their autocorrelation properties and low PAPR, ZC sequences already employed in LTE are a good candidate. With an additional ZF, the same sequence can be used for CSI estimation. Since a single ZC sequence is sent per several OFDM blocks packed within the TIBWB-OFDM block, spectral efficiency suffers minimal decrease.  
Figure 1: Adopted TIBWB–OFDM block frame structure.  
Settling the transmitter block configuration, it is vital to develop the receiver architecture.  
Figure 2 shows an additional new subsystem named \"synchronization and channel estimators\", essential to accomplish signal recovery at first instance, and frequency-domain channel estimation and equalization. It is also necessary to precisely know the transmitted preamble sequence. Thus, stored ZC preamble sequences are a key element of the receiver structure. The respective receiver scheme specifically shows equalizer implementation for the nonlinear IB-DFE type. If the desired receiver structure is a simple linear equalizer, then only TIBWB-OFDM unformatting is performed, obtaining the final estimated sequence as a one-iteration transceiver system. Afterwards, the TIBWB-OFDM structure is composed of equalizers and block unformatting, i.e., block time deinterleaving, bit deinterleaving, demapping, and channel decoding.
## 2 DBQW RTD Photo-Detectors
### Detection Mechanism in DBQW-RTD Waveguide Photo-Detectors  
Inour device, the RTD operates as a waveguide photo-detector by taking advantage of DBQW-RTD photoconductive layers and optical waveguide structure. An RTD-PD optical waveguide works as a waveguide photo-detector for light with energy close or above the waveguide core bandgap energy. The ridge waveguide structure, Figure 2a, offers an advantage over traditional RTD-PDs by providing light confinement along the DBQW plane. The RTD-PD presents the typical RTD nonlinear I-V characteristic with an NDR region which leads to electrical gain. The photo-detection mechanism works as follows. For the low bias condition, electron-hole pairs are created in the depleted part of the waveguide core (that is, the 500 nm undoped In\({}_{0.53}\)Ga\({}_{0.42}\)Al\({}_{0.05}\)As layer) adjacent to the double barriers, see energy band diagram of Figure 2b. However, due to the fact that the electric field is very weak under low bias, the generated electron-hole pairs tend to recombine and neutralize after the creations. As the bias is increased, the hole accumulation density at the hetero-interface increases, resulting in an enhancement of the hole tunneling current. However, for bias voltages below the peak voltage, the current flow is still low and therefore a portion of the photogenerated carriers recombine rather than tunnel through the DBQW structure. When the bias is further increased to the valley voltage, Figure 2b, the effective conductivity of the double barriers drops as the resonant level in the quantum well becomes lower than the Fermi level of the emitter region. Consequently, the band bending on the double barriers increase, resulting in a substantially increase of the hole tunneling current. Once the hole current becomes large enough, the recombination in the photogeneration region decreases to zero, and the photogenerated carriers are collected by the electrodes.  
As described previously, if the photon energy is larger than the waveguide core band-gap energy (here 0.815 eV) the RTD-PD operates as a photoconductive detector. However, if the injected photon energy is slightly lower (e.g., 0.8 eV) the Franz-Keldysh effect [28], which is associated with interband  
Figure 2: (**a**) Schematic diagram of RTD-PD device. The ridge waveguide dimensions were \(\sim\)3 \(\upmu\)m wide and 1 \(\upmu\)m thick. Inset (on top) is shown the schematic diagram (not scaled) of the RTD epi-layer structure. (**b**) RTD-PD unipolar InAlAs-In\({}_{0.53}\)Ga\({}_{0.42}\)Al\({}_{0.05}\)As-InP band energy diagram.  
photon-assisted tunneling, plays also a role in absorption mechanism, _i.e._, the absorption of a photon is considerably enhanced due the presence of the electric field via the Franz-Keldysh effect.
# Classification of Systems and Maintenance Models
## 8 Discussion
When utilizing CBM mathematical models with imperfect inspections, it is commonly assumed that the probabilities of correct and incorrect decisions remain constant and are not influenced by the degradation process parameters or time [2; 52]. When dealing with CBM mathematical models that have non-constant probabilities of correct and incorrect decisions, it is important to note that these probabilities are not influenced by the timing of failure occurrence [98]. Incorporating them into maintenance models can be a difficult and even potentially inaccurate endeavor. We have presented a new decision rule for CBM inspection that differs from previous studies. Our approach considers the decision-making on the time axis, which helps determine the conditional probabilities of a true positive, false negative, false positive, and true negative in relation to a moment of failure occurrence. The proposed approach allows us to correctly insert the probabilities of correct and incorrect decisions into CBM models.
# Research on a Reductive Deep Chlorine Removal Process for Breaking Through the Solid Film Barrier
## 3 Results and Discussion  
### Ascorbic Acid Addition  
Figure 4 illustrates how varying amounts of ascorbic acid impact its dechlorination effect. We conducted the experiment under the following conditions: an initial pH of 3.6, a molar ratio of Cu\({}^{2+}\) to Cl\({}^{-}\) of 1.5, a reaction temperature of 20 \({}^{\circ}\)C, and a reaction time of 20 min. We carried out a one-way experiment with an n(H\({}_{2}\)A)/n(Cl\({}^{-}\)) ratio of 0.4, 0.5, 0.75, 1, 1.25.  
As shown in Figure 4, different amounts of ascorbic acid significantly affect the removal of chloride ions from the solution. As the n(H\({}_{2}\)A)/n(Cl\({}^{-}\)) ratio increases, the chloride ion removal rate initially rises and then gradually decreases. As the n(H\({}_{2}\)A)/n(Cl\({}^{-}\)) ratio rises from 0 to 0.5, the rate of chloride ion removal markedly improves, resulting in a decrease in chloride ion concentration in the solution from 1 g/L to 0.1837 g/L, which corresponds to a removal rate of 81.4%. As the n(H\({}_{2}\)A)/n(Cl\({}^{-}\)) ratio rises from 0.5 to 0.75, the concentration of chloride ions decreases to 0.1363 g/L, resulting in a peak removal rate of 86.2%. However, as the n(H\({}_{2}\)A)/ (Cl\({}^{-}\)) ratio is further increased to 1 and 1.25, the chloride ion removal rates drop to 84.6% and 83.6%, respectively, which are lower than the rate observed at an n(H\({}_{2}\)A)/n(Cl\({}^{-}\)) ratio of 0.75.  
These experimental results indicate that increasing the amount of ascorbic acid significantly enhances the removal rate of chloride ions. This is because ascorbic acid dissociates into ascorbate anions and hydrogen ions in the solution. As the n(H\({}_{2}\)A)/n(Cl\({}^{-}\)) ratio increases, the concentration of ascorbate anions in the solution rises, which increases the frequency of interactions between reactants, thereby promoting the reduction in more copper ions and significantly improving chloride ion removal efficiency. However, when the amount of ascorbic acid is too high, the chloride ion removal rate decreases. This is because, under high concentrations of ascorbic acid, hydrogen ions gradually accumulate during the reaction, and the increasing hydrogen ion concentration inhibits the production of ascorbate anions. This, in turn, affects the coordination between copper ions and ascorbate anions, hindering chloride ion removal. Therefore, to optimize the experimental results, an ascorbic acid addition ratio of n(H\({}_{2}\)A)/n(Cl\({}^{-}\)) = 0.75 was chosen for subsequent studies.
# Dynamic Group Management Scheme for Sustainable and Secure Information Sensing in IoT
## 4 Security and Performance Analysis
### Security Analysis
#### 4.1.2. Sensor Node Eavesdropping and Anonymity  
In the proposed paper, the plain values are the only random number values transmitted by the base station, cluster head, and sensor node. Also, when ID is transmitted, the position of ID is changed reference to random value and 4\(n\) bits stream. Because of that, an attacker could not analyze and recognize transmission values. In addition, anonymity is provided using an ID position change technique.
## 3 Results
### Impact of Data Quality on ANN Performance
Although the above results give an indication of the performance each ANN achieves, depending on the quality and completeness of the offered training data, it is worth presenting the coefficients of determination obtained after training each algorithm. The figures in Table 12 indicate the goodness of fit between the predictions made by the algorithms and the corresponding observed values on the test set. They all appear to be performing extremely well; this contrasting behaviour is reflected upon further in the discussion section.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 3 Materials and Methods
### 3D CNN for Automatic Pulse Rate Estimation  
A 3D CNN classifier structure was developed for both the extraction and classification of unprocessed video streams. The CNN acts as a feature extractor. Its final activations feed two dense layers (multilayer perceptron) that are used to classify pulse rate. The neural network was implemented and trained in Python using TensorFlow and Keras frameworks. All of the predicted data and statistics were processed with Matlab.
###### Abstract  
Advanced persistent threat (APT) refers to a specific form of targeted attack used by a well-organized and skilled adversary to remain undetected while systematically and continuously infiltrating sensitive data. Various APT attack vectors exist, including social engineering techniques such as spear phishing, watering holes, SQL injection, and application repackaging. Various sensors and services are essential for a smartphone to assist in user behavior that involves sensitive information. Resultantly, smartphones have become the main target of APT attacks. Due to the vulnerability of smartphone sensors, several challenges have emerged, including the inadequacy of current methods for detecting APTs. Nevertheless, several existing APT solutions, strategies, and implementations have failed to provide comprehensive solutions. Detecting APT attacks remains challenging due to the lack of attention given to human behavioral factors contributing to APTs, the ambiguity of APT attack trails, and the absence of a clear attack fingerprint. In addition, there is a lack of studies using game theory or fuzzy logic as an artificial intelligence (AI) strategy for detecting APT attacks on smartphone sensors, besides the limited understanding of the attack that may be employed due to the complex nature of APT attacks. Accordingly, this study aimed to deliver a systematic review to report on the extant research concerning APT detection for mobile sensors, applications, and user behavior. The study presents an overview of works performed between 2012 and 2023. In total, 1351 papers were reviewed during the primary search. Subsequently, these papers were processed according to their titles, abstracts, and contents. The resulting papers were selected to address the research questions. A conceptual framework is proposed to incorporate the situational awareness model in line with adopting game theory as an AI technique used to generate APT-based tactics, techniques, and procedures (TTPs) and normal TTPs and cognitive decision making. This framework enhances security awareness and facilitates the detection of APT attacks on smartphone sensors, applications, and user behavior. It supports researchers in exploring the most significant papers on APTs related to mobile sensors, services, applications, and detection techniques using AI.  
cyber cognitive situational awareness (CCSA); Joint Directors of Laboratories (JDL); MITRE framework; user behavior; spear phishing; game theory +
Footnote †: journal: Applied Sciences
# Robust Image Watermarking Algorithm Based on ASIFT against Geometric Attacks
## 1 Introduction
As for the tilted image, the feature extracted from the image can be of a small quantity. Concretely, the SIFT algorithm has the feature of scale invariance instead of affine invariance, which can result in the limitation that extraction for an image whose shooting angle changes with a large angle is difficult. In this paper, a novel robust watermarking based on affine-scale-invariance feature transform (ASIFT) [27] in the wavelet domain is presented. Firstly, DWT is performed on the host image for three times, and SVD is operated on the selected low frequency component in horizontal and vertical directions (LL) sub-band. Secondly, the watermark information is embedded into the obtained three-level LL sub-band. The ASIFT points are saved as feature keys for the correction of attacks. Thus, anti-attacks capability can be attained with the help of matching ASIFT feature points in the extraction phase. Finally, experimental results demonstrate that the proposed scheme is imperceptible and resilient to common image processing such as Gaussian noise, salt and pepper noise, speckle noise, median filters, cropping, and so on. Especially for geometric attacks, it has better performance than SIFT-based watermarking. The remainder of this paper is arranged as follows: in Section 2, the related work is reviewed, including the theories of DWT, SVD, and ASIFT; Section 3 introduces the distortion correction by the ASIFT points; Section 4 gives concrete watermark embedding and extraction procedure; comparison with previous schemes in evaluation of robustness and imperceptibility and demonstration of the advantages in the proposed scheme are given in Section 5; conclusions and future work are illustrated finally in Section 6.