esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Graphic** & **h** & **BER (Range)** \\ \hline Figure 8a & h1 = [0.18 0.85 0.32] & 0 \\ Figure 8b & h2 = [0.302 0.725 0.456] & \(10^{-1}\)–\(10^{-2}\) \\ Figure 8c & h3 = [0.407 0.815 0.407] & \(10^{-1}\)–\(10^{-2}\) \\ \hline \hline \multicolumn{3}{c}{**Turbo equalization with 5 it.**} \\ \hline
**Graphic** & **h** & **BER (range)** \\ \hline Figure 9a & h1 = [0.18 0.85 0.32] & \(10^{-3}\) \\ Figure 9b & h2 = [0.302 0.725 0.456] & \(10^{-2}\)–\(10^{-3}\) \\ Figure 9c & h3 = [0.407 0.815 0.407] & \(10^{-2}\)–\(10^{-3}\) \\ \hline \hline \multicolumn{3}{c}{**Separate MMSE equalization and LDPC decoding with 40 it.**} \\ \hline
**Graphic** & **h** & **BER (range)** \\ \hline Figure 10a & h1 = [0.18 0.85 0.32] & \(10^{-5}\)–\(10^{-6}\) \\ Figure 11a & h2 = [0.302 0.725 0.456] & \(10^{0}\)–\(10^{-1}\) \\ Figure 12a & h3 = [0.407 0.815 0.407] & \(10^{0}\)–\(10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: BER vs. SNR in an ISI channel, at the SNR of 5 dB, of the proposed system, turbo equalization, and separate MMSE equalization with LDPC decoding, for different impulse responses h.  
The impulse responses were not chosen randomly; they can be found in the specialized literature and are referenced in the text. Figure 13: BER vs. SNR, LDPC decoder–20 iterations, not puncturing the first 2Z\({}_{\text{c}}\) bits: (**a**) h1 = [0.18 0.85 0.32]; **(b)** h2 = [0.302 0.725 0.456]; and (c) h3 = [0.407 0.815 0.407]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Puncturing the First 2Z\({}_{\text{c}}\) bits** & \\ \hline
**Graphic** & **h** & **BER (Range)** \\ \hline Figure 8a & h1 = [0.18 0.85 0.32] & 0 \\ Figure 8b & h2 = [0.302 0.725 0.456] & \(10^{-1}\)–\(10^{-2}\) or (\(1/2\times 10^{-1}\)) \\ Figure 8c & h3 = [0.407 0.815 0.407] & \(10^{-1}\)–\(10^{-2}\) \\ \hline \hline
**Not puncturing the first 2Z\({}_{\text{c}}\) bits** & \\ \hline
**Graphic** & **h** & **BER (range)** \\ \hline Figure 13a & h1 = [0.18 0.85 0.32] & 0 \\ Figure 13b & h2 = [0.302 0.725 0.456] & \(10^{-1}\)–\(10^{-2}\) or (\(1/9\times 10^{-1}\)) \\ Figure 13c & h3 = [0.407 0.815 0.407] & \(10^{-2}\)–\(10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: BER vs. SNR in an ISI channel, at the SNR of 5 dB, of our proposed system, puncturing or not the first 2Z\({}_{\text{c}}\) bits.
## 2 Instrument for Observation
### The GMR's Solar Observation Mode  
The GMR adopts the stable elevation-over-azimuth antenna positioner system and we improved the antenna servo control system of the GMR so that it could track and observe the solar microwave radiation. It works on two modes, i.e., the meteorological observation mode and the solar observation mode.  
(1) Meteorological observation mode. Generally, the GMR works on meteorological observation mode to obtain atmospheric TBs which are used to retrieve atmospheric temperature and humidity profiles.  
(2) The solar observation mode. This observation mode is specially developed for tracking and observing the solar radiation. The azimuth and elevation observing angles are sent to the antenna servo control system so that the antenna beam can scan the sun once the radiometer enters the solar observation mode. Firstly, the antenna system would be adjusted to control its antenna beam pointing to the position of the sun and tuning the antenna beam to scan the sun. During azimuth scanning, the antenna is moved to around the actual sun position using several Polar Plane Indicator (PPI) scans in the same elevation, by adding a step angle \(\Delta\theta_{A}\) on the azimuth of the sun. In order to obtain a raster scanning for the sun, we need to repeat PPI scanning at different elevations. Secondly, the antenna is adjusted so that the sun is completely out of the beam to scan the sky using Range Height Indicator (RHI) scanning by adding an elevation step angle \(\Delta\theta_{E}\) on the antenna elevation. Measurements in the direction of the sun and in another direction (sky background) were differenced to get an estimate of the solar TB increment. The antenna azimuth and elevation angle can be given as:  
\[Az_{GMR} =Az_{SUN}+\Delta\theta_{A}, \tag{1}\] \[El_{GMR} =El_{SUN}+\Delta\theta_{E}, \tag{2}\]  
where \(Az_{GMR}\) and \(El_{GMR}\) are the antenna azimuth and elevation angle. \(Az_{SUN}\) and \(El_{SUN}\) are the solar azimuth and elevation angle. The azimuth and elevation step angle \(\Delta\theta_{A}\) and \(\Delta\theta_{E}\) are the relative position between the antenna beam pointing and the sun. Since the sun is moving along the sky within this time interval, we adjusted to control the antenna pointing to scan the sun, and we need to re-calculate the solar azimuth and elevation in real time and it is used to derive the relative position between the antenna beam pointing and the sun for each recorded data point. Each azimuth and elevation scanning can last up to 3 min at each frequency in 29 scanning angles, and the observations were made in a totally automated fashion. The solar observations are typically performed once or twice a week on sunny days and the sun is observed for more than 3 h a day.
# Shallow Velocity Estimation by Multiples for Monochannel Boomer Surveys
## 2 Travel-Time Inversion Algorithm  
Figure 1 displays a simple 1D Earth model, composed of two homogeneous layers with horizontal interfaces: seawater, with a thickness _Wd_, and an underlying shallow formation, with a thickness _Lt_. The P-wave propagation velocities are _Vw_ and _Vlayer_, respectively. Along the sea surface, a source and a receiver are located at points A and C with an offset \(X\). The ray paths of three different signals are drawn, assuming that the ray bending can be neglected: the primary reflection from seafloor AM*C (dashed green line), a primary reflection AA*BB*C from the layer bottom (black continuous line) and a peg-leg multiple AA*B*C*M*M*C (dotted blue line). Another event that can be used optionally is the direct arrival of AMC.  
In a field experiment, the measured values are the _Tabc_ and _Tpl_ travel times from the primary ABC and peg-leg multiple. In the following text, we use bold fonts for known values, to distinguish them from the unknowns during the computation flow; the unknowns are in plain, italic font. We assume that the seawater velocity _Vw_ is also known, as a standard value of 1500 m/s is very often found. However, seasonal temperatures may affect this velocity by varying it in a range from 1440 to 1540 m/s (see, e.g., [13]). If temperature and salinity are known, the formula of Mackenzie [14] can be used for it. The water depth _Wd_ may be known from bathymetric maps or measured either directly by sonar or indirectly by inverting the _Tvd_ travel time of the primary reflection from the seafloor. The offset \(X\) is measured along the cable length that is trailing the source and receiver. If significant deviations are expected, due to feathering effects [15; 16], an alternative measurement for \(X\) is provided by the _Td_ travel time of the direct arrival, multiplied by the seawater velocity _Vw_. Thus, in any of these different ways, we may assume that \(X\), _Wd_, and _Vw_, as well as _Tabc_ and _Tpl_, are known. With this information, the layer thickness _Lt_ and its velocity _Vlayer_ can be estimated.
# A Novel Adaptive Image Data Hiding and Encryption Scheme Using Constructive Image Abstraction
## 3 Experimental Results and Analysis
### Analysis of Security
#### 3.2.1 Perception by Vision  
Figure 5 displays the images produced by our algorithm at various stages. The HDR RGBE case is shown first followed by the case for the LDR image. Figure 5a,b show the original HDR image by directly display it without tone-mapped operation and the tone-mapped results, respectively. Figure 5c exhibits the abstracted stego images concealed around 8.487 million bits of hidden messages, while Figure 5d shows the contours of the abstracted stego images. Figure 5e shows the encrypted tone-mapped image which successfully shelters the outlines and detail features without revealing any meaningful information. Finally, Figure 5f shows the image decryption result. Figure 5g-j are the corresponding results for the LDR case. The visual perception demonstrates that our encryption scheme is visually secure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**ID** & **Name** & **Resolution** & **EC (bits)** & **ER (bpp)** & \({}^{*}\)**PSNR (dB)** & \({}^{*}\)**SSIM** \\ \hline HDR 1 & Desk & 644 \(\times\) 874 & 6,995,448 & 12.428486 & 45.506 & 0.999875 \\ HDR 2 & Atrium Night & 760 \(\times\) 1016 & 8,523,403 & 11.038930 & 45.399 & 0.999836 \\ HDR 3 & 507 & 1072 \(\times\) 712 & 8,487,159 & 11.119559 & 50.949 & 0.999736 \\ HDR 4 & Bird of Paradise Flower & 2464 \(\times\) 1632 & 33,503,279 & 8.331563 & 47.327 & 0.999845 \\ HDR 5 & Windows & 524 \(\times\) 800 & 4,658,477 & 11.112780 & 43.934 & 0.999314 \\ \hline LDR 1 & Pond & 1072 \(\times\) 603 & 5,731,397 & 8.866421 & 39.213 & 0.995143 \\ LDR 2 & Kodim07 & 768 \(\times\) 512 & 3,461,004 & 8.801788 & 38.960 & 0.996886 \\ LDR 3 & F15 & 960 \(\times\) 540 & 4,597,217 & 8.868089 & 39.533 & 0.995428 \\ LDR 4 & NCHU Main Gate & 1024 \(\times\) 634 & 5,660,622 & 8.719166 & 37.400 & 0.969324 \\ LDR 5 & Lena & 512 \(\times\) 512 & 2,295,636 & 8.757156 & 39.087 & 0.996439 \\ \hline \end{tabular} \({}^{*}\) For HDR images, the values are tone-mapped results.  
\end{table}
Table 3: Test images and the payloads.  
Figure 5: _Cont._
## 6 Conclusions  
Utilizing routers within a domain to provide security does have an impact on the performance of the network since it introduces significant delays due to the equipment. There are some relatively simple steps that can be taken to improve the performance.  
By investigating the theoretical aspects of delays through routers and carrying out a series of measurements it has been possible to improve the mathematical model of delays encountered by a packet as it transverses a domain. It has also been possible to quantify the delays to understand which components are more significant which leads to a series of rules that can be used as best practice when designing large networks.  
There are significant differences in the delays experienced using different versions of the OS in the router. A more advanced OS adds delays to the basic routing process but if other functionality is required then an advanced OS has to be used.  
Optimal performance can be gained by not having ACLs enabled in a router. Clearly it is not possible to remove the ACLs from all routers within a domain but there are gains to be made by reducing the number of routers that have ACLs enabled. When using an Advanced OS the number of rules in an ACL is insignificant. Since a domain has a common security policy then it should be possible to optimize the placement of ACL rules to ensure that the minimum number of routers in a domain use an ACL.  
Having completed optimization on the number of routers requiring an ACL then using a basic OS for a router without ACLs and using advanced OS for the routers that do require an ACL will show an overall improvement of performance.  
The results of this paper have demonstrated that it is possible to optimize the performance of a network without modification to its underlying hardware or security policy. The most significant finding is to ensure that the OS loaded into routers is appropriate for the functionality required and so understanding the requirements of the individual routers in a domain is essential.  
By investigating a domain which has a single security policy and therefore allows functionality of routers to be allocated this paper considers how it can be optimized. It shows that by ensuring the OS with the appropriate functionality is used an improvement of performance will be gained. This is particularly important when the security is provided by ACLs. By utilizing an OS with advanced functionality an improvement around 130% is possible.  
Furthermore this paper shows that by moving the ACLs to only the egress/ingress points of a domain that a performance improvement of the order of 320% can be gained over using a basic OS with no optimization or in excess of 30% over using an advanced OS. The justification for doing so is that within a domain there is a common security policy and so it is possible to reduce the number of routers that have to be configured with an ACL and therefore reduce the delays through them.
# An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density
## 4 Proposed Partitioning and Overlapping Clustering Method
### Partitioning Clustering
#### 4.1.2 Cluster Expansion  
The selection of the initial edge is a pivotal aspect of the proposed clustering method's cluster expansion phase. Various strategies for choosing this initial edge were investigated, including prioritizing edges with the lowest, highest, or average total degree, as well as random selection. Notably, when prioritizing edges based on their highest or lowest edge degrees, experimental results revealed significant variations in cluster sizes and their topological characteristics, even across different relative density (\(\delta_{r}\)) settings. This intriguing phenomenon is further discussed in the 'Future Work' section.  
This study introduces an innovative clustering approach that commences by selecting an edge with an average degree \(Avg_{deg}(\mathcal{G},e_{i})\) to initiate the clustering process and expand the cluster. This method consistently generates clusters with comparable sizes and topologies across various experiments. As the cluster expands, it continuously monitors its relative density \(\delta_{r}(c_{i})\) using Equation (5) to ensure adherence to the desired density specified by the user \(U(\delta_{r})\). At each expansion step, the algorithm strategically chooses adjacent edges based on their average degree. This iterative process entails a step-by-step expansion of the cluster by methodically including the chosen adjacent edge. Consequently, it leads to the refinement and enhancement of both the cluster's structure and its relative density.  
During each iteration, the most recently added edge is accepted if it contributes to bringing the relative density, \(\delta_{r}(c_{i})\), of the current cluster (\(c_{i}\)) closer to the specified target density, \(U(\delta_{r})\). If a cluster expansion does not precisely reach the desired value of \(U(\delta_{r})\), the algorithm compares the existing and expanded density deviation and selects the cluster with the relative density closest to \(U(\delta_{r})\).
## 4 Conclusions  
In this paper, the damage characteristics of red sandstone and limestone targets under different impact velocities were studied by means of experiments, numerical simulations and Forrestal semiempirical penetration calculations. The following conclusions were obtained within the scope of this paper:  
1. As the target speed increases, the number of cracks in the same region increases. At the same speed, since the energy absorption effect of limestone is weaker than that of red sandstone, the cracks formed on the limestone target are few and wide, whereas the cracks formed on the red sandstone target are dense and fine.
2. At the same speed, the impact angle of the projectile has little effect on the axial penetration depth and acceleration but has obvious effect on the radial penetration depth and acceleration. Compared with limestone, red sandstone is more sensitive to the impact angle of the projectile.
3. The limit velocity of the projectile penetrating the 1 m-thick red sandstone target was 900-1000 m/s, whereas the limit velocity of the projectile penetrating the 1 m-thick limestone target was 1100-1200 m/s. Therefore, the penetration resistance of limestone is approximately 23.8% stronger than that of red sandstone.
4. The numerical simulation results are in good agreement with the experimental results, and the error is less than 10%.
5. When the target strength changes from 80 MPa to 160 MPa, the depth of the invasion decreases in three stages (i.e., a gentle drop, a sharp drop and a gentle decline), and the invasive acceleration is a three-stage decline (i.e., a gentle rise, a sharp rise and a gentle rise).  
Through experimental and numerical simulation research in this study, the damage characteristics and damage differences between red sandstone and limestone were obtained under low-speed to medium-high-speed penetration. Additionally, the correctness of the numerical simulation model was demonstrated. This investigation provides a reference for future research studies of projectile penetration into red sandstone and limestone and has a  
Figure 16: Relation between penetration acceleration and target strength change.  
certain significance for the development of projectile design and underground protection technology.  
Conceptualization, X.Z. ([PERSON]) and W.Y.; data curation, X.Z. ([PERSON]) and W.Y.; formal analysis, X.Z. ([PERSON]), W.Y., X.W., W.Z., Z.L., H.H. and X.Z. ([PERSON]); funding acquisition, W.Y.; investigation, X.Z. ([PERSON]); supervision, W.Y.; writing--original draft, X.Z. ([PERSON]); writing--review and editing, X.Z. ([PERSON]) and W.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Multi-Label Weighted Contrastive Cross-Modal Hashing
## 4 Experiment
### Hyperparameter Sensitivity Analysis
#### 4.6.2 Trade-Off Parameter \(\gamma\)  
As presented in Equation (20), hyperparameter \(\gamma\) is to balance the two components of multi-label weighted contrastive loss, i.e., intra- and inter-modal weighted InfoNCE losses \(\mathcal{L}_{intra}^{\textit{weight}}\) and \(\mathcal{L}_{inter}^{\textit{weight}}\). To analyze the effect by \(\mathcal{L}_{intra}^{\textit{weight}}\) and \(\mathcal{L}_{inter}^{\textit{weight}}\), we recorded the performance change by varying the value of \(\gamma\) on MIRFLICKR-25K, NUS-WIDE and MS COCO datasets. Figure 8 reports that our method MLWCH achieves the best MAP score when \(\gamma\) is set to 0.1. Albeit \(\mathcal{L}_{intra}^{\textit{weight}}\) can constrain the intra-modality semantic structure, the performance gain is relatively minor for the cross-modal hashing task. In contrast, inter-modal weighted InfoNEC loss has a relatively greater effect on hashing learning. We conjecture that this result is mainly because inter-modal contrastive strategy is more important for eliminating cross-modal heterogeneity. The above results also clarify that by selecting an appropriate value of \(\gamma\), our model can achieve impressive performance.
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 4 Illustration of the Risk Assessment with Smart Room Scenario
### LP Formulation for IoT Cyber Resource Allocation  
Assume that type \(v\) and \(w\) are independent and require a resource allocation decision separately, while type \(x\), \(y\), and \(z\) are network related, and the resource allocation of the three types needs to be optimized. Hence, an LP model for type \(x\), \(y\), and \(z\) is developed, and the result is discussed after the execution of the model.  
Assumptions for the LP model  
1. Decision variables: Defense probability for type \(x\), \(y\), and \(z\), respectively
2. The minimum defense probability: \(x=0.90\); \(y=0.90\); \(z=0.90\)
3. The maximum defense probability (operational feasibility constraint): all \(x\), \(y\), \(z=0.99\)
4. Financial constraints to achieve the defense probability of 0.94: \[\$3,\!000,\!000\ x+\$500,\!000\ y\leq\$3,\!290,\!000,\] \[\$3,\!000,\!000\ x+\$500,\!000\ y\geq\$3,\!150,\!000,\] \[\$500,\!000\ y+\$2,\!000,\!000\ z\leq\$2,\!350,\!000,\] \[\$3,\!000,\!000\ x+\$2,\!000,\!000\ z\leq\$4,\!700,\!000,\] \[\$3,\!000,\!000\ x+\$2,\!000,\!000\ z\geq\$4,\!500,\!000,\]
5. Defense probability to an operational feasibility constraint between \(y\) and \(z\): \(y+z<=1.91\).  
Based on the assumptions, the following LP model is formulated: Minimize GTC = 7,155,000 - 2,000,000 \(x-1,\!500,\!000\ y-3,\!000,\!000\ z\)  
Subject to  
\[3,\!000,\!000\ x+500,\!000\ y\leq 3,\!290,\!000,\] \[3,\!000,\!000\ x+500,\!000\ y\geq 3,\!150,\!000,\] \[500,\!000\ y+2,\!000,\!000\ z\leq 2,\!350,\!000,\] \[3,\!000,\!000\ x+2,\!000,\!000\ z\leq 4,\!700,\!000,\] \[3,\!000,\!000\ x+2,\!000,\!000\ z\geq 4,\!500,\!000,\] \[y+z\leq 1.91\]  
This linear programming (LP) model was executed using a free online LP, and the result is discussed below. Source: [[https://www.wolframalpha.com/widgets/view.jsp?id=daa12](https://www.wolframalpha.com/widgets/view.jsp?id=daa12) bbf5e4 daec7b363737d6d496120]([https://www.wolframalpha.com/widgets/view.jsp?id=daa12](https://www.wolframalpha.com/widgets/view.jsp?id=daa12) bbf5e4 daec7b363737d6d496120)).  
Table 3 shows that Option 3 is the lowest cost with type \(y\) assigned to the defense probability of 0.98, type \(x\) assigned to the defense probability of 0.933, and type \(z\) assigned to the defense probability of 0.93. The optimal total investment cost is $304,999. Using the LP model, Option 3 generates a lower total cost than Option 2, even with a smaller investment cost. This illustration shows the effectiveness of the LP model in complicated IoT cyber resource allocation decisions. A variety of sensitivity analyses can be conducted by changing the assumptions, which will help managers be more confident about using the LP model for resource allocation decisions. As the NIST's four Implementation Tiers model suggests [49], each organization is different in their cyber defense capability. The suggested LP model can effectively accommodate the unique situations of each organization.
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 1 Introduction
This work seeks to use recent measurement data from the areas considered in [17], increase the sample space to enhance the universality of the results obtained by adding UNILAG (another urban settlement in Lagos, Nigeria), analyze the network performance of an already deployed 4G LTE network in those areas, and perform minimal optimization using the PSO algorithm to suggest practical solutions to the problems of the 4G LTE network in the environments considered. This study aims to delve into the complexities and scope of a live 4G LTE network, analyze its performance through selected propagation measurements, and provide optimized solutions to the problems faced by the network in the area. Cutting-edge drive test equipment from Huawei Technologies is adopted to ensure congruence with real-life conditions. Since this study seeks to address 4G LTE shortcomings and comprehensively evaluate its key performance indicators in Nigeria, the measurements will focus on vital indicators, otherwise referred to as key performance indicators (KPIs), such as RSRP, RSRQ, RSSI, PCC PHY DL throughput, and PDCP DL throughput. All measurements will be conducted at a working frequency of 1876.6 MHz. The propagation measurements will be taken over a distance of one kilometer at 100-m intervals. All measurements will begin at a reference distance of 100 m from the base station. The results will be extracted, scrutinized, and refined in MATLAB after the collection of measurements. The primary goal is to develop novel models that are tailored to the unique characteristics of the environments investigated in this study.