esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Now, in this paper, we are providing a proof using a widely-known cryptographic model that better describes the algebraic computations and the algorithms or oracles that the attacker has to access. Moreover, very recently, in [14] [Section 1.1], NIST warns of the vulnerability of cryptographic protocols whose security lies on algebraic problems based on integer factorization or the discrete logarithm problem against an adversary in possession of a large-scale fault-tolerant quantum computer. The protocol introduced in [12] bases its security precisely on the difficulty of the discrete logarithm problem in the chosen group. However, in [15], the authors introduce a new algebraic setting to extend all these key management protocols, as is later shown in [16] for the case of the protocols introduced in [9], and there is no known algorithm that solves the underlying algebraic problem even with the use of a quantum computer. Using similar developments to those in [16], we could also use the protocols introduced in [12] for the new algebraic setting, but, as we will show, our attack proves the vulnerability of the authenticated Group Key Management given that it uses the nature of its own protocol and not the algebraic setting. The structure of the paper is as follows. In Section 2, we provide all the methods and terminology that are used throughout the paper. We start by recalling the authenticated initial group key agreement, as it is introduced in [12], where the underlying security is based on the so-called decisional DDH. Then, we establish the security model that will be followed to cryptanalyze the protocol and recall a well-known and widespread setting to use this protocol, i.e., the case of elliptic curves, which allows us to analyze a case study. In Section 3, the reader can find a detailed explanation of the active attack, which shows that [PERSON] et al. 's proposal for GDH2 [12] (denoted by A-GDH2) fails against it, and, as a particular case, [PERSON] et al. 's original CLIQUES IKA.2 implementation introduced in [9]. In Section 4, we provide a case study using elliptic curves and propose strategies in the auxiliary key agreement, i.e., in the rekeying protocol, in order to take total control of the group or the possibility for the attacker to leave the group without letting the users know that the attacker was listening to all communications for a period of time. We conclude the paper with some final considerations.
# Hybrid Spine Simulator Prototype for X-ray Free Pedicle Screws Fixation Training
## 2 Materials and Methods
### Software Architecture
#### 2.2.4 Virtual X-ray Visualization  
This module generates realistic virtual fluoroscopic images of the current surgical scene that comprises: the surgical tool, the patient-specific vertebrae, a virtual human body, and a C-arm 3D model (the \"Virtual C-Arm\"). The pose of the vertebrae and the tool are expressed in the same reference system thanks to the Camera Calibration module. The human body (a standard 3D model, with size compatible with the patient-specific virtual spine) is manually registered according to the anatomical correct positioning of the spine The Virtual C-Arm is scaled to a realistic size (the source-to-detector distance, SDD, is 0.120 m as in [35]), and a \"virtual isocenter\" is placed at a distance of 0.64 m from the source (source-to-isocenter distance, SID, of 0.64 m).  
X-ray simulation is obtained through the implementation of a virtual camera (the \"X-ray Camera\"), positioned at the Virtual C-Arm source. The FOV of the X-ray Camera and its clipping planes are manually tuned according to the size of the Virtual C-Arm.  
The X-ray beam projection of a C-arm device is commonly defined using two rotation angles (Figure 5): the left/right anterior oblique (LAO/RAO) angle, \(\alpha\); and the caudal/cranial (CAUD/CRAN) angle, \(\beta\) (in the literature, these are also referred to as the angular and orbital rotation angles, respectively) [36]. A custom Unity script-component was implemented to allow the user to rotate the Virtual C-Arm around its isocenter, adjusting \(\alpha\) and \(\beta\) values with keyboard inputs to obtain different image projections. Moreover, this script also implements the Virtual C-Arm translation along the three main axes.  
The X-ray Camera uses a custom replacement shader to render the vertebral meshes and the tracked instrumentation: the \"X-ray effect\" is implemented with a colored, semi-transparent shader, with no backface culling to also render polygons that are facing away from the viewer (Figure 6).
## 4 Case Study Application: Towards an Efficient Terminal Container in the Port of Augusta
### The Port of Augusta in the Eastern Sicily Port System  
The Eastern Sicily port system is a public body in charge of the management of the two Sicilian ports of Catania and Augusta. The Port of Catania, a medium-sized city of about 300.000 inhabitants, is an international seaport with a high proximity to the city centre, the airport, the railway station and the main logistic centres. This proximity makes the port of Catania home to a mixed use of its space, ranging from cruise, leisure, yachting activities, vessels' construction, Ro-Ro and container freight traffic. The Port of Augusta, a small city of about 35.000 inhabitants, is one of the Strategic Ports of the European Union of the TEN-T (Trans-European Transport Network) \"CORE\" network and the largest natural harbour in the lower Mediterranean area. It is location is very close to an important petroleum centre and a big portion of its harbour is reserved to the industrial port. The port also counts on a military base, a Ro-Ro Terminal, a container terminal and a port/city (Figure 3).  
Currently, the only container traffic managed by the Eastern Sicily Port System is held by the Port of Catania and it amounts at 59.746 TEUs/year. The terminal business includes among its clients Maersk, CMA CGM, ZIM and Tarros line services connecting Catania to Italian and Mediterranean ports. Nevertheless, the ship capacity in those services does not exceed 1300 TEU, meaning that the port operates as a small feeder port of regional importance. As mentioned in the introduction, due to the lack of space in the port of Catania, the port of Augusta will undertake the major container traffic, which makes its level of development of crucial importance to preserve the current business and forfuture plans. The current capacities of the Augusta container terminal are presented in the next section.
## 5 Discussion
### Comparison of K-Means Clustering Results and ISODATA Clustering Results  
The evaluation of the clustering results for buildings and roads can indicate how many buildings and roads are included in the clustering results. From Table A1, we can find that the 0123 combination only needs 7-8 categories to include more than 99% of buildings and roads, and each evaluation metric is the best (1_Recall is the coverage rate of buildings and roads). In region 2, the accuracy of the 0123-combination image is 53.82% higher than the accuracy of the original image and the TCT image. The difference of each evaluation metric between the 0123 and 0132 combinations is within 0.05, which indicates that buildings and roads have similar spectral characteristics in the red band and NIR band. This is consistent with the spectral characteristics of buildings and roads in the principles of using red light bands to distinguish artificial ground objects and grasslands and using near-infrared and infrared bands to construct a building index (NDBI). In region 4, although the 1230, 1203, 1023, 0231 and 0213 combinations contain all buildings and roads with only 8-9 categories, there are too many background ground objects interspersed, so the separability of buildings and roads from the background ground objects is not as good as the 0123 combination.  
As shown in Table 2, the 0123-combination images of region 4 and image B have the highest F1-score and Kappa, and it can also be seen from Figure 6 that the buildings and roads extracted from the 0123-combination images are more complete and clearer. The clustering results, although none of them are very good, can show that the pseudo-TCT makes it easier to distinguish buildings and roads from other ground objects. Although K-Means clustering and ISODATA clustering evaluated the separability of buildings and roads from background ground objects, there are still some ground objects where the separability needs to be verified. The clustering of buildings and roads is only used to select the best pseudo-TCT combination and to compare the intra-class consistency of the pseudo-TCT images, the original images and the TCT images, and is not used to extract buildings and roads. Only using pseudo-TCT and clustering cannot extract buildings and roads well; if we want to obtain better extraction results, we need to combine other methods.
## 1 Introduction  
### Background and Content of the Study  
The adoption of deep learning techniques in real-time monitoring can save a lot of manpower in various scenarios. For example, mask-wearing is a highly effective measure to prevent the pandemic, especially for vulnerable groups such as children, the elderly, and people with underlying health problems. Incorrect mask-wearing primarily refers to situations where the nose or mouth is exposed. It is vital to wear masks correctly to ensure maximum protection. Once before, the strict containment policies kept COVID-19-positivecases at a minimum in public places, so incorrect mask-wearing might not have posed a significant impact. However, the change to a more lenient policy has increased the risk of exposure to infected individuals, making correct mask-wearing even more important. The requirements for mask-wearing vary across different public settings. In high-risk areas such as fever clinics in hospitals, strict adherence to mask-wearing protocols is essential due to a higher risk of virus transmission. Real-time monitoring of individuals who wear masks incorrectly or have them removed is pivotal. In other public places, occasional incorrect mask-wearing or temporary removal of masks may have a less noticeable impact. In such cases, prioritizing accuracy is critical to avoid unnecessary alerts that could disrupt the overall crowd experience. It is expensive for cloud-based devices to provide real-time monitoring; hence, off-cloud detection provided by terminal devices is more preferable. However, most of these low-cost devices currently available have very limited GPU capability, necessitating the use of neural networks with fewer parameters. The main challenge in mask-wearing monitoring lies in the accurate identification of individuals who have a small portion of their mouths or noses exposed while wearing masks. Therefore, it is necessary to introduce as few parameters as possible to make the model focus on detailed features.  
There are two primary categories of mask-wearing detection methods. The first category utilizes well-established face detection techniques to locate the position of the faces. Subsequently, the face region was extracted and fed into a classifier network to determine if the mask is worn correctly. Alternatively, a direct one-stage object detection algorithm is used to simultaneously detect the face and assess the presence of a mask. However, since the face detection technique in the first category is well-developed, further performance improvement is hard to achieve. Therefore, this paper concentrates on enhancing the classifier network of the first category and exploring the second approach as shown in Figure 1.
# Gaussian Quantum Trajectories for the Variational Simulation of Open Quantum-Optical Systems
## Appendix A Photon-Counting, Homodyne and Heterodyne Unravelings
### Homodyne Detection  
For homodyne detection, the light leaving the cavity is mixed with a strong coherent signal with the same frequency, the Local Oscillator (LO). Because of the high intensity, no individual photons are distinguished, but a continuous measurement of the photocurrent is performed. Following the discussion of [78], the setup is described as follows (see Figure 11).  
Take the input field (the light leaking from the cavity) \(\hat{\mathtt{a}}\) and the LO \(\hat{b}\). In the beam splitter, these modes recombine to:  
\[\hat{c} =\sqrt{T}\hat{a}+i\sqrt{1-T}\hat{b}\] \[\hat{d} =i\sqrt{1-T}\hat{a}+\sqrt{T}\hat{b}, \tag{11}\]  
where \(T\) is the transmissivity and \(1-T\) the reflectivity. The signals measured in the two detectors are then given by:  
\[\hat{c}^{\dagger}\hat{c} =T\,\hat{\mathtt{a}}^{\dagger}\,\hat{\mathtt{a}}+(1-T)\hat{b}^{ \dagger}\hat{b}+i\sqrt{T(1-T)}(\hat{\mathtt{a}}^{\dagger}\,\hat{b}-\hat{b}^{ \dagger}\,\hat{\mathtt{a}})\] \[\hat{d}^{\dagger}\hat{d} =(1-T)\,\hat{\mathtt{a}}^{\dagger}\,\hat{\mathtt{a}}+T\hat{b}^{ \dagger}\hat{b}-i\sqrt{T(1-T)}(\hat{\mathtt{a}}^{\dagger}\,\hat{b}-\hat{b}^{ \dagger}\,\hat{\mathtt{a}}). \tag{12}\]  
As one can see, these operators are time-independent if the local oscillator has the same frequency as the input field. Now, use for the LO mode a coherent state \(\hat{b}=\hat{\beta}=|\beta|e^{i\phi_{L}}\) with large amplitude \(|\beta|\) and phase \(\phi_{L}\). With this substitution, the last term in the signal operators (12) is proportional to:  
\[\hat{X}_{\phi}=\frac{1}{2}(\hat{\mathtt{a}}\,e^{-i\phi}+\hat{\mathtt{a}}^{ \dagger}\,e^{i\phi}) \tag{13}\]  
where \(\phi=\phi_{L}+\pi/2\). \(\hat{X}_{\phi}\) is a quadrature variable, and the most common ones are the conjugate \(\hat{X}:=\hat{X}_{0}\) and \(\hat{P}:=\hat{X}_{\pi/2}\). There are now two possibilities for the detection of \(\left\langle\hat{X}_{\phi}\right\rangle\). In the so-called ordinary homodyne detection, \(T\approx 1\) and only the photon flux in detector one is measured. The input signal term is negligible with respect to the other terms, and the LO-term is constant, except from shot noise (and can hence be manually subtracted afterwards) so that only the interference term remains.  
Figure 11: Setup for homodyne (and heterodyne) detection.  
In balanced homodyne detection, a beam splitter with \(T=0.5\) is used instead, and the signals of both detectors are directly subtracted. Whether ordinary or balanced detection is used has no influence on the quantum trajectory; there, only the quadrature variable that is measured is important. In practical experimental settings, the use of homodyne detection schemes is paramount in a variety of systems. Notable examples are in quantum cryptography [79] and the detection of'spooky action at a distance' [80].
## 2 Results and Discussion
### The SFN and PEITC Treatment Controls Expression of Virulence Factors of V. cholerae
(**a**) The genes’ expression was assessed using RT-qPCR method. Transcriptional levels of various virulence-related genes were analyzed by qRT-PCR in the stringent response inducing conditions, namely treatment with SFN (\(\frac{1}{4}\) MIC), PEITC (1/8 MIC), SHX (0.8 mg/mL) for 15, 45, and 60 min at 37 \({}^{\circ}\)C. The relative transcription level of each gene was compared to the _recA_ gene used as an internal control. \(\Delta\Delta\)Ct was calculated as: \(\Delta\)Ct (test)\(-\Delta\)Ct (calibrator). Ratio\(=\) efficiency\({}^{-\Delta\Delta\)Ct. (**b**) CT protein was detected by Western blot using anti-CTB rabbit monoclonal antibodies. Samples were collected after 20 h. Toxin production was quantified densitometrically by the Quantity One (Bio-Rad). The results presented are mean values from 3 independent experiments. The Student’s _t_-test statistical significance is marked with asterisks, \({}^{***}\)\(p<0.001\).
# Integration of Wavelet Denoising and HHT Applied to the Analysis of Bridge Dynamic Characteristics
## 1 Introduction
[29] improved the HHT method, applied it to the processing of the GNSS monitoring data of Baishazhou Yangtze River Bridge in Wuhan, and verified the effectiveness and rationality of this method when used for modal parameters' identification from non-stationary vibration signals. With regard to the development of EMD-related algorithms, [PERSON] et al. [30] proposed a noise-assisted data analysis method called ensemble empirical mode decomposition (EEMD), which suppresses the mode mixing problem of EMD by adding white noise to the initial data many times. However, the performance of the EEMD algorithm depends largely on the noise amplitude and the number of trials. The IMFs generated by EEMD become highly polluted and even yield pseudo-components when noises with inappropriate amplitudes are added and the number of trials is changed inappropriately [31]. Furthermore, the time required by EEMD-related algorithms may increase when the number of trails is increased [32]. On the basis of the remarkable effect of noise when the HHT method is applied to non-stationary data and in consideration of the excellent time-frequency localization properties in the field of filtering and denoising, a time-frequency analysis method based on the combination of wavelet threshold denoising and HHT is proposed in this study. The GNSS dynamic monitoring data of Sutong Bridge are adopted as the study object, to identify the first natural frequency of the bridge structure. The implementation process is presented. The method implements noise reduction preprocessing on the dynamic monitoring data to obtain reduced decomposition layers in the EMD decomposition process and decrease the marginal effects on the quality of useful signal decomposition. The vibration component is reconstructed according to the correlation of each component and subsequently serves as input data for Hilbert spectral analysis to identify the first natural frequency of the bridge structure. The denoising effect of the experimental analysis on simulated noisy signals proves the effectiveness of the proposed method. The application analysis results of real bridge monitoring data indicate that the proposed method can highlight the dynamic characteristics of bridge structure vibration signals in noisy environments and effectively identify the first natural frequency, which is conducive to the scientific evaluation of the safety status of the bridge structure.
###### Abstract  
This paper proposes a novel autoencoder-based neural network for compressing and reconstructing underwater acoustic signals collected by Directional Frequency Analysis and Recording sonobuoys. To improve both signal compression rates and reconstruction performance, we integrate Residual Vector Quantization and a Compensation Module into the decoding process to effectively compensate for quantization errors. Additionally, an unstructured pruning technique is applied to the encoder to minimize computational load and parameters, addressing the battery limitations of sonobuoys. Experimental results demonstrate that the proposed method reduces the data transmission size by approximately 31.25% compared to the conventional autoencoder-based method. Moreover, the spectral mean square errors are reduced by 60.58% for continuous wave signals and 55.25% for linear frequency modulation signals under realistic air channel simulations.  
DIFAR sonobuoy; autoencoder; air channel modeling; signal reconstruction Article
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 8 Modulation Techniques
### Related Works
#### 8.4.1 MCS Selection  
An access point must select the most accurate MCS for each station. A higher MCS means a larger throughput if the radio link is strong enough. Otherwise, the bit error rate begins to dominate and will corrupt most of the frames. However, the standard does not propose any mechanisms to select the most appropriate one.  
[PERSON] et al. [178] proposed a new path loss model for 5 GHz indoor environments. They showed that when using low bandwidth and high transmission power for transmission, the access point gain increases by choosing a higher MCS. Moreover, as the RSSI increases, the number of used spatial streams also increases. [PERSON] et al. [179] took into account the interference and noise level. A station estimates the channel quality according to the successful transmissions using a particle filter [180]. Then, with respect to the channel quality, the station selects the best-fitted MCS to achieve higher reliability and performance.  
[PERSON] et al. [181] proposed a deep learning approach to select the best MCS. Link adaptation was modeled as a multi-label multi-class classification and predicted the performance of the system for both the optimal MCS and suboptimal MCS. They applied a deep convolutional neural network to predict the performance with a dataset generated with Matlab. The loss function gives a larger penalty for false positives; the MCS should not have been selected since it jeopardizes the reliability.  
An access point may have precise knowledge of the CSI of each station and should allocate an RU and MCS to each of them. This scenario corresponds to an optimization problem. [PERSON] et al. [182] proposed a mixed-integer nonlinear programming formulation to maximize the total system throughput. They assumed that there was a specific Bit Error Rate (BER) model per MCS. A station may also be attached to multiple access points, which makes the link adaptation more challenging [183].
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Cluster-Based Routing Protocols  
Clustering theory is a method of arranging nodes with the same geographic neighborhood characteristic into multiple different groups, mainly solving the problem of resource scarcity in the FANET. It helps increase network scalability, reduce routing overhead and maximize throughput, saving the energy of UAVs.
# Experimental Study on Cooling Performance of a Hybrid Microchannel and Jet Impingement Heat Sink
## 2 Experimental Apparatus
### Test Section  
Figure 1b shows the schematic of the test module with an electric heater on the proposed heat sink. The heating device is a printed circuit on an AlN substrate. The heating area is 1.4 cm \(\times\) 1.5 cm. The thickness of the AlN substrate is 0.6 mm. A DC power supply with controlled voltage is used to provide a DC current to the heater. A power meter (QINGZHI-8716C) is used to measure the voltage, current and power of the heating device. Silicone grease (ZF-12) is used between the AlN substrate and the heat sink. The measurements of thermal contact resistance and AlN thermal conductivity are described in Section 2.3. Thermal insulation of the test module is evaluated, and the heat loss is less than 1%, which can be neglected in this work.  
Figure 1: Schematic of closed-loop test apparatus (**a**), the test module (**b**), and the structure of the hybrid heat sink (**c**).  
As shown in Figure 1c, the proposed heat sink consists of many thin copper plates with slots to form microchannels and micro-jets. A unit is composed of 5 plates (plates 1-5 in Figure 1c) in a certain order, and there are 25 microchannel units in a heat sink. In plate 4, as shown in Figure 1c, there are 26 arrays of microchannels with a width of 0.5 mm and a length of 10 mm on a plate with a thickness of 0.2 mm. The coolant flows through a 6 mm diameter channel in plates 1 and 2, and then the fluid is fed into the microchannels of plate 4 through the inflow plenum on plate 3. The coolant is impinged on the bottom of the microchannel through the slots on plate 4. The coolant leaves the bottom channels on plates 2, 3 and 5, and flows into the outflow plenum on plate 1. The hybrid heat sink has a highly-dense micro-jet array (~100 jets per cm\({}^{2}\) with jet width of 0.5 mm) and a separate design of inflow and outflow passages, and an efficient fluid distribution and convective heat transfer can be expected on the heating surface.