esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G-IPAKA: An Improved Primary Authentication and Key Agreement Protocol for 5G Networks
## 5 Formal Verification of the 5G-IPAKA Protocol
Similarly, \(\{RAND^{\prime\prime}{}_{SN}||K^{\prime\prime}{}_{SEAF}||SUBI^{\prime\prime}| |RAND||H^{\prime\prime}||\,(HXRES*)^{\prime}\}_{K_{SN,HN}}=term(<r^{\prime},3>)\) must originate on a server strand \(t^{\prime}\). Since \(RAND\) uniquely originates in \(\sum\), \(t^{\prime}=t\), so \(RAND^{\prime\prime}{}_{SN}=RAND_{SN}\) and \(RAND_{SN}\) originate on \(term(<r^{\prime},2>)\). Through Assumptions 1 and 3, \(RAND_{SN}\) originates on \(term(<r,2>)\). Since \(RAND_{SN}\) uniquely originates in \(\sum\), \(r^{\prime}=r\). However, \(r^{\prime}\in\text{Resp}_{\text{I}}\) and \(r\in\text{Resp}_{\text{I}}\), \(r^{\prime}\
eq r\). Hence, \(t\) is not a server strand of Definition 1. If \(t\) is a server strand of Definition 2, then \(t\in\text{Serv}_{\text{I}}\{UE,\,SN,\,HN,\,SUPI,\,SUICI^{\prime},\,SNN,\,RAND,\,K_{SEAF},\,RAND,\,AUTN^{\prime},\,(HXRES*)^{\prime},\,Syncf,\,AUTS^{\prime}\}\), where \(SUPI\subset SUICI^{\prime}\), \(x^{\prime}\subset AUTN^{\prime},x^{\prime}\subset(HXRES*)^{\prime}\) and \(x^{\prime}\subset AUTS^{\prime}\). Similarly, \(\{RAND_{SN}||SUICI^{\prime}||SNN\}_{K_{SN,HN}}=term(<t,1>)\) must originate on a responder strand \(r^{\prime}\). Since \(RAND_{SN}\) uniquely originates in \(\sum\), \(r^{\prime}=r\), so \(SUICI^{\prime}=SUICI\) and \(x^{\prime}=x\) according to Assumption 1. Hence, \(AUTN^{\prime}=AUTN\), \((HXRES*)^{\prime}=HXRES*\) and \(AUTS^{\prime}=AUTS\). Since \(BK=KDF(K,x\cdot y\cdot G||SNN)\), \(BK\
otin\mathcal{K}_{P}\) according to Assumption 2, \(MAC-S=f^{*}_{\text{I}}(BK,SQNUE||RAND||AMF_{0})\subset AUTS\subset term(<t,3>)\) must originate on a unique initiator strand \(s\in\text{Init}_{\text{I}}\{UE,\,SN,\,HN,\,SUICI,\,RAND^{\prime\prime}{}_{SN},\,RAND,\,AUTN^{\prime\prime},\,Syncf,\,AUTS\}\) according to Assumption 3, where \(SQN^{\prime\prime}\subset AUTN^{\prime\prime}\). Similarly, \(MAC^{\prime\prime}\subset AUTN^{\prime\prime}\subset term(<s,2>)\) must originate on a server strand \(t^{\prime}\). Since \(RAND\) uniquely originates in \(\sum\), \(t^{\prime}=t\), so \(SQN^{\prime\prime}=SQN\) and \(AUTN^{\prime\prime}=AUTN\). According to \(t\), \(K_{SEAF}\) is encrypted by \(K_{SN,HN}\). According to Assumption 2, \(K_{SEAF}\
otin\mathcal{K}_{P}\). Because \(MAC_{UE,2}=HMAC(K_{SEAF},RAND_{SN}||Syncf||AUTS)\), \(MAC_{UE,2}\subset term(<r,5>)\) must originate on an initiator strand \(s^{\prime}\). Since \(x\) uniquely originates in \(\sum\), \(s^{\prime}=s\), so \(RAND^{\prime\prime}{}_{SN}=RAND_{SN}\). \(\square\)  
According to Theorems 5 and 6, \(SN\) successfully authenticates \(UE\) and \(HN\), and the injection agreement [28; 29; 30] can be established.
## The Free Interface Model
### Free Interface Model with Constant Source  
The most simple case in the free interface model (3)-(6) is when we take the source term \(S\) to be constant in time and space. Indeed, according to ([40] Section 2.3), when all quantities are independent of \(X\), the pressure becomes hydrostatic \(p=g\cos\theta(h-Z)\), and the source takes on a constant value  
\[S=g(-\sin\theta+\mu_{s}\cos\theta), \tag{10}\]  
where \(g>0\) is gravitational acceleration, \(\theta>0\) the constant slope angle (note that this sign convention differs from [40; 41]), and \(\mu_{s}=\tan\delta>0\) the friction coefficient with \(\delta\) the friction angle related to the material. The source term is thus the result of the balance between the driving force due to gravity (\(-g\sin\theta<0\)) and the friction force (\(\mu_{s}g\cos\theta>0\)). Indeed according to ([40], Proposition 2.1), for the source term (10), the solution to the system (3)-(6) is an exact solution to the two-dimensional original viscoplastic model (1) and (2) without dependency on \(X\). This means that the free interface model (3)-(6) with a constant source term (10) exactly describes the simple shear flows of (1) and (2), corresponding to the configuration shown in Figure 4.  
We observe that with (10) and \(\mu_{s}=\tan\delta\), the static equilibrium condition (6) is simplified and becomes \(\tan\theta\leq\mu_{s}\), i.e.,  
\[\theta\leq\delta, \tag{11}\]  
meaning that the internal friction must at least neutralize the gravity force due to the slope. This is a necessary condition for the existence of a solution to the free interface model (3)-(6). Indeed, if this condition is not satisfied, we expect that \"\(b=0\)\", meaning that the entire layer immediately flows down the inclined plane.  
The remainder of the paper is devoted to the evaluation of this constant source term model.  
Figure 4: Simplified flow configuration consisting of a uniform flowing layer over a uniform static layer, both parallel to the rigid bed of slope angle \(\theta\). The static/flowing interface position is \(b(t)\) and the total thickness of the mass \(h\) is constant.
# Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning
## 4 Proposed System
### Model Architecture  
Regarding NeMo's automatic speech recognition collection, QuartzNet [6] and Jasper [12] are two end-to-end model architectures worth mentioning due to their role in the current work.  
Jasper is a block architecture designed to ease fast GPU inference. The block architecture is composed of one convolutional pre-processing block, B \(\times\) R blocks (where B represents the number of blocks and R the number of sub-blocks), and three convolutional post-processing blocks. Each block input is connected to the last sub-block using a residual connection. Each of the R sub-blocks applies a set of four operations: a 1D convolution, batch normalization, ReLU, and dropout. Models with this architecture are trained using the CTC loss function. Jasper models achieve state-of-the-art results of 2.95% when using a beam-search decoder and an external language model.  
QuartzNet derives from Jasper's B \(\times\) R block architecture and has the goal of achieving state-of-the-art results while using models with fewer parameters. QuartzNet performs similar operations, the only difference being the replacement of the 1D convolution with a 1D time-channel separable convolution (compared in Figure 2). The 1D time-channel separable convolution is an implementation of depthwise separable convolutions which can be separated into two other convolutions, a 1D depthwise convolution and a pointwise convolution (see Figure 3). The 1D depthwise convolutional layer performs convolutions across time, and a pointwise convolutional layer performs a 1 \(\times\) 1 convolution across features/channels [6; 32], which enables the QuartzNet architecture to achieve state-of-the-art results while drastically reducing the number of parameters.  
Figure 2: Standard convolution (**left**) and depthwise separable convolution (**right**) [33].
## 2 Materials and Methods
### Experimental Design and Data Collection
#### 2.1.2 Experiment Equipment  
The corn drying experiment is based on a batch-type grain drying simulation experiment system, and the working principle and structure of the system are shown in Figures 1 and 2. This system employs a countercurrent drying section combination model, which is a mainstream model in current corn drying production. This type of drying system is representative for simulating corn drying experiments [28; 29]. Figure 1, wherein parts marked as 2, 3, and 4, indicate the arrangement of temperature and humidity sensors for real-time collection of temperature and humidity characteristics of the corn drying process [30]. The data are then transmitted via electrical signals to a programmable logic controller (PLC) module for storage. The drying section area is large; so, following the five-point sampling method, five groups of temperature and humidity monitoring points are arranged, with three additional groups of monitoring points in the remaining positions. The specific distribution of the system is shown in Figure 3.  
Figure 1: Grain drying simulation test system workflow sketch.  
Figure 2: Grain drying simulation experiment system. (1: Drying section; 2: Hot air duct; 3: Tempering section; 4: Electric hot air furnace; 5: Grain discharge section; 6: Feed opening; 7: Control box; 8: Grain elevator).  
The names of the measured temperature and humidity are as follows: upper outlet temperature, upper outlet humidity, lower outlet temperature, lower outlet humidity, drying section A temperature, drying section B temperature, drying section C temperature, drying section E temperature, drying section A humidity, drying section B humidity, drying section C humidity, drying section D humidity, drying section E humidity, hot air pipeline temperature, and hot air pipeline humidity.
# Detection of Malware by Deep Learning as CNN-LSTM Machine Learning Techniques in Real Time
## 5 Research Methodology
### Features Selection  
Feature selection follows feature extraction, which entails the discovery of even additional features [30]. Choosing features from a pool of newly recognized attributes is called feature selection, and it plays a significant role in improving accuracy, simplifying the model, and reducing overfitting. In the past, researchers have attempted to spot malicious software by employing a wide variety of feature categorization algorithms. In this research, the feature rank approach is used a lot because it works well to choose relevant features for building malware detection models [31].
## 7 Results and Discussion
### Simulation Results with Sunlight
Where a more accurate spectra are necessary and specific information such as atmospheric aerosol concentrations are available, we refer the reader to more sophisticated models such as SMARTS2 [64; 65] or MODTRAN [66], a highly complex tool used by researchers and government agencies. Nevertheless, given that the results of this study apply to a fundamental determinant of UOWC, i.e., the carrier, it will therefore apply to all modulations and multiple-access schemes regardless. These results further highlight an additional layer of complications to consider upon the findings of [PERSON] et al. that showed how the effects of geometric loss and attenuation created spatial variability in the received optical power, with implications for modulation schemes and receiver array designs [67]. Validating the findings of this study at scale may be extremely resource intensive. However, it may be possible to simulate the conditions in a scaled version using a solar simulator and a partitioned water tank to introduce stratification. Absorption and scattering qualities could be introduced using agents such as Nigrosin dye and Maalox antacid [68]. Critical design decisions will need to be made to prevent total internal reflection within each partition. It is preferable that a high-end solar simulator is selected that produces a class-A spectral match. Alternatively, conducting a partial simulation-based study may be more effective by gathering vertical Chl-\(a\) concentration profiles and the solar spectral distribution in the field, then simulating with established UOWC geometric models to estimate the O-SNR. However, to our knowledge, it has been difficult to find datasets with both Chl-\(a\) and solar spectral information collected during the same voyage, which is a crucial factor for such a study. Furthermore, the schemes to discriminate these effects may be addressed from a signaling or hardware point of view and must be verified through additional studies (mobility models, etc.). We aim to explore these in our future works.
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 4 Results and Discussion
### In-Air Temperature Profile Measurements  
Figure 8 shows the surface temperature of each cell in the battery pack during in-air experiments. Figure 8b specifically displays the temperature of each cell during the first in-air cycle. As the initial cell temperature starts below 40 \({}^{\circ}\)C, a reduction in surface temperature is observed during the charging of the LIB. During discharge in the air (Figure 8b), a dip in ambient temperature occurs due to air flowing around the cells. The surface temperature gradually increases during discharge, reaching a peak of around 43 \(\pm\) 0.3 \({}^{\circ}\)C.  
On the ground, the maximum temperature reached was approximately 39 \({}^{\circ}\)C, indicating a 6.5 \(\pm\) 0.3 \({}^{\circ}\)C rise in surface temperature during in-air discharge. This temperature increase is attributed to the high C-rate discharge performed in the air. Sudden spikes in temperature at 0.167 h and after 0.33 h were caused by attitude corrections. T4 in Figure 8a,b showed lower temperatures compared to the other cells, likely due to cell imbalance in the battery pack [29; 47].  
Figure 8: Surface temperature data collected by BMS during random flight patterns of the drone, incorporating ambient temperature (TA) using a 4-cell LIB pack. (**a**) A consistent pattern across 20 cycles performed by the drone. TA dips during flight, and each cell exhibits varying temperatures. (**b**) One discharge cycle while flying. It shows an increase in temperature over time, with the discharge cycle lasting less than an hour.  
Compared to the BMS developed by [PERSON] et al. [50], the proposed BMS achieves higher C-rates with in-air data, indicating varied temperatures among the cells in the battery pack. As shown in Figure 8a, during the first five cycles, T2 exhibited a higher temperature than the other cells. After the fifth cycle, T3 showed a greater increase in temperature compared to T2. Temperature peaks were first observed in the seventh in-air cycle because the current exceeded a 4C discharge rate for the first time within the 20 cycles. Notably, the highest temperature reached was around 55 \({}^{\circ}\)C, which is 16 \(\pm\) 0.5 \({}^{\circ}\)C higher than the peak temperature observed on the ground, and 10 \(\pm\) 0.5 \({}^{\circ}\)C higher than the first in-air cycle. Overall, temperatures observed during in-air experiments were higher than those recorded during on-ground experiments.
# B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
## 2 Related Work
[22] proposed a dilated dual attention U-Net (D2A U-Net) for COVID-19 lesion segmentation in CT images. The dilated convolution module in a model decoder refines the decoding process and improves segmentation accuracy. Further, a dual attention mechanism, which is inserted to the skip connection and model decoder is utilized to refine feature maps and therefore reduce the semantic gap between different levels of the model. A U-Net based COVID-19 CT images segmentation network using an attention mechanism was proposed by [23]. They incorporated a spatial and a channel attention mechanism to a U-Net architecture to capture rich contextual relationships for better feature representation and, consequently, to enhance the network performance. Previous work has used attention and CAMs to find the region in the image where the lesion was. Our proposed method can localize B-line regions in LUS videos both spatially and temporally despite being trained in a weakly-supervised fashion. We leverage temporal analysis networks, and use spatiotemporal attention to find the most important frames (i.e., B-line frames) and localize B-line regions within a video. Indeed, the ability to detect B-line frames in the LUS video is necessary for clinical application as B-line artefacts appear at arbitrary frames within an LUS video. Furthermore, detecting B-line regions in the LUS video along with the B-line frames would be the first step towards the quantification of the severity of the disease.
# Automatic TV Logo Identification for Advertisement Detection without Prior Data
## 3 Dataset Description
The levels are the following:  
* Level 1: Streams with opaque logos in a static position and background distinguishable. Figure 4: Comparison between the general TVI channel logo and the logo used to advertise an upcoming program. (**a**) Program scene where the typical colored version of the channel logo is visible in the top left corner. (**b**) Grayscale version of the logo on an advertisement of an upcoming program. Figure 3: Eurosport logo blended with the background as seen in the final part of the logo. Figure 2: Comparison between the two logos present on RTP1b stream. (**a**) RTP1b special 60 years logo. (**b**) General RTP1b logo in the immediate next frame. * Level 2: Streams with logos in a static position, where the logo is often very similar to the background or indistinguishable. * Level 3: Streams with varying or animated logos, where the logo is often similar or blends to the background. Following the definition of the difficulty levels and the inherent characteristics of the streams, the following classification was made:  
* Level 1: SIC, TVI, and FOX HD;
* Level 2: RTP1a, RTP2, and CMTV;
* Level 3: RTP1b and Eurosport. To perform an objective comparison with state-of-the-art methods, the dataset presented in [3] was also used. It is composed of only three artificially generated streams using recordings of three different Portuguese channels and some repeated advertisements. The total duration of these streams is 2575 s and the general characteristics can be described as follows:  
* Stream SICNot_BL: Recording of a news program of the SIC channel, with three sudden cuts to advertisement. * Stream TVI_BL: Three different programs of the TVI channel, divided by two advertisement blocks containing the same advertisements. * Stream RTP_BL: Similar structure as the SICNot_BL stream but with recordings of the RTP1 channel. Based on the characteristics of these streams and on the criteria defined for the three difficulty levels, they were assigned to the lowest level of difficulty. The classification of the collected set of streams into the defined difficulty levels resulted in an unbalanced distribution, where most of the content was classified with either difficulty level 1 or 2. Additionally, due to the single classification of long streams, some content with challenging details was absorbed into a unique classification. For a more detailed analysis and characterization of the streams, the dataset was divided into smaller segments, summarized in Table 4. This split in smaller streams was accomplished by applying the proposed difficulty levels to contiguous portions of the complete streams, without tampering with the advertisement blocks nor the programming content.
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 4 Materials and Methods
### Routing Protocol Motivation  
One of the main challenges in WBANs is the excessive energy consumption during communication and packet transfer between sensor nodes. This indicates that the significant limitation of the sensor nodes' energy resources affects the overall WBAN performance, including network lifetime and reliability. Various energy-efficient clustering-based routing protocols have been proposed to mitigate this challenge to prolong the network's lifetime. These routing protocols are based on multi-hop communication (i.e., two-hop) to reduce the distance between the sensor nodes and the sink, decreasing the consumed energy. Some of these protocols are highlighted in Table 1. Despite their goal of energy efficiency, existing cluster-based protocols suffer from inappropriate selection criteria for parent nodes and an uneven distribution of traffic load among the deployed sensor nodes. A common problem of these existing protocols is the overloading of nodes near the sink node, as they are frequently selected to forward packets, leading to rapid battery depletion and creating energy holes within the network. Furthermore, the selection criteria of parent nodes for forwarding data introduces inefficiencies in energy consumption and network reliability.  
Given these challenges, our proposed routing protocol aims to tackle these issues by implementing a more reliable and efficient approach. Specifically, our protocol focuses on refining the selection criteria for parent nodes and ensuring the traffic burden is evenly distributed among sensor nodes. By improving the criteria for selecting forwarders, the protocol aims to optimize energy usage and avoid situations where certain nodes are overloaded, while others remain underutilized. This helps lessen the energy hole and promotes a more balanced utilization of network resources. Additionally, our proposed protocol also emphasizes the importance of reliability in WBANs. In this context, timely delivery (i.e., delay) and successful packet transfer are important factors, especially in scenarios such as ongoing patient monitoring, where undetected life-threatening events can have severe repercussions. By addressing these reliability concerns, our protocol aims to enhance the quality of patient monitoring by ensuring that data are delivered accurately and on time.
# Robust Anti-Jamming Algorithm Based on Transmit/Receive Time-Sharing Technology
## 5 Simulation Experimental Verification  
In order to verify the universal adaptability of the robust anti-jamming algorithm in navigation receivers, simulation experimental verification was performed to evaluate the performance of the navigation receiver under extensive conditions.  
The robust anti-jamming algorithm is generally applicable to typical narrowband interference scenarios. Figure 15 shows that under different interference characteristics, such as intensity and bandwidth, the robust anti-interference algorithm has a noticeable improvement effect on CNR loss compared with the traditional algorithm. It is assumed that the CNR of the signal is 50 dB\(\cdot\)Hz, the interference bandwidth is from 0.5 MHz to 2 MHz, the step is 0.1 MHz, the interference-to-signal ratio is from 40 dB to 60 dB, and the step is 1 dB. Figure 15a shows the performance of the traditional anti-jamming algorithm. As the interference-to-signal ratio increases, the CNR loss increases to 4 dB\(\cdot\)Hz. Figure 15b shows the performance of the robust anti-jamming algorithm. The CNR loss remains stable, and the maximum CNR loss is about 1 dB\(\cdot\)Hz.  
The robust anti-jamming algorithm is suitable for different signal environments and improves the receiver performance in strong signal scenarios. The performance impact of the original CNR on the data loss is more evident with a shorter signal reception interval. Figure 16 show that the robust anti-jamming algorithm significantly improves information loss compared with the traditional algorithm under different signal power and navigation receiver receiving time slots. The CNR of the signal without interference is assumed to be 45 dB\(\cdot\)Hz to 65 dB\(\cdot\)Hz, covering the conventional navigation receiver environment and strong signal scenarios. The signal receiving time slot was considered from 15 ms to 1.5 s, and the data proportion of the loss information in the receiving time slot was evaluated. Figure 16a show the performance of traditional anti-jamming algorithms. As the signal strength increases, the proportion of information loss increases, and the data loss is more pronounced in a short time slot. The proportion of information loss can be increased to 8%. Figure 16b show the performance of the robust anti-jamming algorithm. The proportion of information loss is always below 0.1%, showing a minimal increase o  
Figure 15: The CNR loss under different interference characteristics. (**a**) The performance of the traditional anti-jamming algorithm. (**b**) The performance of the robust anti-jamming algorithm.