esa-sceva/satcom-chunk-collection huggingface dataset:
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 3 Related Work
### Internet of Things and Smart Cities  
The IoT refers to a network of physical objects embedded with software, sensors, and other technologies, thereby enabling connectivity and data exchange among devices and systems. Some primary reasons for leveraging the IoT include (i) improved efficiency [102]; (ii) data-driven decision making [103]; (iii) enchanted user experience [104]; and (iv) remote monitoring and controlling [105].  
The concept of the \"Internet of Things\" was first introduced as a term in a speech in 1985 and has since evolved to be driven by the combination of multiple technologies such as ubiquitous computing, shared sensors, increasingly powerful embedded systems, and ML. It finds applications in various sectors of human life, including healthcare systems, transportation, smart homes, and energy. Along these lines, the smart city concept utilizes IoT technologies and data-driven solutions to enhance the efficiency, sustainability, and quality of life of its residents [106]. Incorporating IoT devices [107], sensors, data analytics, and various other technologies allows for the real-time utilization of data, thus enabling better decision-making and resource management across different domains such as transportation [108], energy [109], healthcare [110], public safety [111], and governance [112]. A summary of the discussed works is illustrated in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Challenges** & **Papers** & **Focus** & **Solution/Protocol** \\ \hline  & [62; 65; 66; 70; 73] & NDN/DTN architectures & NoD, ICN-over-LoRa \\  & [77] & NDN/DTN platforms for IoT & UMOBILE \\  & [76] & & RICE \\  & [62; 67; 68; 69] & Adaptive multiprotocols & NoD and ML \\  & & for smart cities & \\  & [63] & Caching in IoT & EFPCaching \\  & [71] & & CoAP \\  & [72] & IoT protocols and concepts & Content islands \\  & [74; 75] & & CCN and reflexive forwarding \\  & [81] & & DID \\ Heterogeneity, integration & [83] & & ICN Data muling \\ with IP-based networks, & [84] & Name-based push and pull service \\ data prioritization, & [85] & Disaster scenarios and prioritization of interests & Popularity estimation scheme \\ security & [87] & Prioritization of interests & Image prioritization method \\ connectivity & [86] & & NREP scheme \\  & [88] & Opportunistic named functions \\  & [89] & & Reputation-based trust \\  & [94] & Protocols/Solutions & iMMM-VNDN \\  & [95] & DADT \\  & [91] & V-NDN \\  & [90] & ICN VANETs architecture & SEVeN \\  & [97] & CSPC and PPCCR mechanism \\  & [98] & Caching in VANETs & Content prefetching optimization \\  & [99] & RSUC and ReA schemes \\  & [100] & Prioritization in VANETs & LISIC protocol \\  & [101] & Push-based VNDN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of ICN/NDN-based literature works.
# Exploring ISAC: Information-Theoretic Insights
## 2 Pre-ISAC: Sensing (Radar) vs. Communication
### Wireless Communication Systems  
In a communication system, a transmitter (Tx) aims to transfer either data bits or source samples (such as audio or video file samples) to a distant receiver (Rx). The data or source information is encoded onto a transmitted waveform, which the receiver then collects and analyzes to estimate the transmitted information. Performance metrics commonly considered for communication systems include energy or spectral efficiency, which measure how many bits of information are communicated using a given energy budget or bandwidth, respectively.  
For data transmission, robustness of communication is typically measured by the bit-error rate (BER), symbol-error rate (SER), or frame-error rate (FER), which indicate the likelihood of errors in the received data due to channel disturbances. In source communication, robustness is either measured by the bit-error rate or more often by distortion metrics such as the average mean-squared error. These performance metrics are especially pertinent in traditional wireless communication systems, which predominantly operate in the 2.4 GHz band. The main differences between radar and communication systems are shown in Table 2.
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 3 Methodology
### Profile-Wise Correction  
To transform the query cloud \(\mathbf{M}\) profile-wise, the estimated parameters must not differ too much for neighboring profiles \(\hat{\mathbf{x}}_{i-1}\), \(\hat{\mathbf{x}}_{i}\). After all, significant discrepancies would eventually lead to discontinuities in the point cloud. In general, adjacent parameters agree well if the number of points is high enough and object normals are well-distributed (see Section 3.6). However, to minimize any remaining inconsistencies, a smoothing process is introduced for all transformation parameters separately. Thus, we first smooth the transformation parameters \(\hat{\mathbf{x}}\) with a moving average filter and then correct the query point cloud by  
\[\mathbf{m}_{\hat{\mathbf{i}}}^{\mathbf{c}}=L_{\hat{q}}(\mathbf{m}_{\hat{ \mathbf{i}}})+\hat{\mathbf{t}}\;. \tag{13}\]  
Note that the selection of the filter width is hardware dependent. We set the width to 50 in our study.  
The correction assumes the reference point cloud to be error-free. When we align point clouds from two scanners or opposite driving directions, we want to modify both  
Figure 6: The transformation of MLS point clouds into the IMU body frame enables us to validate the co-registration of an asymmetric two-laser scanner setup (**left**) and to introduce a weighting function into the constraint GHM model (**right**).  
point clouds \(\mathbf{R}\), \(\mathbf{M}\) equally, though. Let us therefore decompose the transformation into two subsequent rotations and translations:  
\[L_{q^{\prime}}\Big{(}L_{q^{\prime}}(\mathbf{m})\Big{)}+\frac{1}{2}\mathbf{t}+ \frac{1}{2}\mathbf{t}=\mathbf{r}\;. \tag{14}\]  
In contrast to its original definition (cf. Equation (3)), \(L_{q^{\prime}}\) rotates around the same axis \(\mathbf{e}\) but with half the angle \(\theta/2\). By rearranging, we obtain the relation for adjusting two point clouds \(\mathbf{r}\), \(\mathbf{m}\) to each other with  
\[L_{q^{\prime}}(\mathbf{m})+L_{q^{\prime}}^{*}\left(\frac{1}{2}\mathbf{t} \right)=L_{q^{\prime}}^{*}\left(\mathbf{r}-\frac{1}{2}\mathbf{t}\right)\;, \tag{15}\]  
where \(q^{\prime*}\) is the conjugate quaternion and \(L_{q^{\prime}}^{*}\) thus describes the rotation around -\(\mathbf{e}\) by \(\theta/2\). For a sample calculation, the reader is referred to Appendix A.2.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 3 Proposed Hardware Architecture
### Overview of the Architecture  
It comprises various essential components. It includes a memory unit (MU) responsible for placing different results as discussed in Section 3.2. The routing networks (RNs) facilitate the data transfer between different locations as detailed in Section 3.3. The read-only memory (ROM) is utilized to retrieve the necessary BEC parameters as outlined in Section 3.4. The ALU, which executes computations essential to the BEC process, is described in Section 3.5. Finally, the FSM provides the required control signals (Section 3.6).  
In addition to the aforementioned essential components, pipelined registers are placed before the ALU as depicted in Figure 1. This placement enables the efficient overlapping of the read, execute, and write-back stages, leading to an improved throughput and minimized critical path delays. The architecture is meticulously designed based on the parameters specified by the National Institute of Standards and Technology (NIST). By incorporating these components and employing pipelining techniques, the proposed architecture aims to achieve superior performance and efficiency in BEC computations.
## 5 Simulation Results and Analyses
### Comparison and Analysis of the Packet Loss Ratio  
Table 2 provides the packet loss ratio after round 1200 for the six algorithms when 400 nodes were considered in the network. The packet loss ratio is defined in this paper as the ratio of the number of data packets that the CHNs send to the number of data packets that the SN receives during the whole simulation process. As we can see from the table, the packet loss ratio of the proposed ACOCR is the lowest. The LEACH, which performs the worst, has about a 1.62 times higher packet loss ratio than the proposed ACOCR does. This is because the ACOCR adopts the improved ACO algorithm to find the optimal routing paths, which can reduce the risk of packet loss.  
Figure 8 demonstrates the received packets by the SN versus the number of rounds for the six algorithms when 400 nodes were considered in the network. The more packets the SN receives, the more efficient the algorithm is. Apparently, the ACOCR algorithm has the best performance, the efficiency of which is 18.6%, 27.4%, 44.1%, 60.9%, and 84.1% higher than that of the EOCA, CUWSN, LEACH-ANT, DUCS, and LEACH, respectively, in round 1200.
# Combination of Permanent and Variable Load Is Dependent
## 2 Materials and Methods
### A Large Load Group  
Assume that a detached housing estate comprises 100 houses with trussed roofs and there are similar 10 trusses in all houses based on one design and one design combination load. One may assume that one truss has 15 failure sources. Thus, the estate has a total of 15,000 failure sources and physical load combinations on the roofs. Assume further that the total area of the estate is 1 km\({}^{2}\), i.e., one hectare per house, as such communities are common in Europe where the Eurocodes are applied. It is reasonable to assume that the climatic loads, e.g., wind and snow, are the same for the whole estate over such a small area. Consequently, the variable loads have a similar effect on all the houses and their failure sources and the load combinations. As with the roof girder example explained above, there is a full correlation between the permanent load and the variable load and, therefore, DLC must be applied.  
A critical issue is what is the failure probability of the roofs of this community. If the loads are combined independently and the structural design is made according to the current Eurocodes the failure probability of each failure source P\({}_{\text{E50}}\)= 1/15000. However, the simultaneous variable loads over all roofs make the combination dependent as explained in next section. Interlacing of high permanent loads and high variable loads does not occur and the failure probability is about ten times higher P\({}_{\text{E50}}\)= 1/1500. This example shows that the group effect alone is sufficient to show that the load should be combined dependently.
## Appendix A Solving Process of the TICC  
### Expectation Part: Cluster Assignment  
At this step, TICC assigns observations to the given clusters. At the very start, k-means is adopted to obtain prior distribution for the initial assignment and then the initial cluster parameter \(\Theta\) can be obtained. With fixing \(\Theta\), T subsequences is assigned to K clusters according to the optimization:  
\[\text{minimize}\sum_{i=1}^{K}\sum_{X_{t}\in P_{i}}-\ell\ell(X_{t},\Theta_{i})+ \beta 1\{X_{t-1}\
otin P_{i}\} \tag{1}\]  
to maximize the two objectives, the log likelihood \(\ell\ell(X_{t},\Theta_{i})\) and the temporal consistency \(1\{X_{t-1}\
otin P_{i}\}\) are used, which determines how possible adjacent points belong to the same cluster. The solving process is a dynamic programming approach. The process involves finding the path with the minimum cost from timestamp 1 to T, where the cost of a node is the negative log likelihood of the point being assigned to a particular cluster. Algorithm 1 provides a pseudo code for this process.  
```
1:given\(\beta>0,-\ell\ell(i,j)=\) negative log likelihood of point \(i\) assigned to cluster \(j\)
2:initialize prev_cost = list of \(K\) zeros
3: curr_cost = list of \(K\) zeros
4: prev_path = list of K empty lists
5: curr_path = list of K empty lists
6:for\(i=1\) to \(T\)do
7:for\(j=1\) to \(T\)do
8: min_index = index of minimum value of prev_cost
9:ifprev_cost[minIndex] + \(\beta\) > prev_cost[\(j\)] then
10: curr_cost[\(j\)] = PrevCost[\(j\)] - \(\ell\ell(i,j)\)
11: curr_path[\(j\)] = PrevPath[\(j\)].append[\(j\)]
12:else
13: curr_cost[\(j\)] = prev_cost[minindex] + \(\beta\) - \(\ell\ell(i,j)\)
14: curr_path[\(j\)] = prev_path[minindex].append[\(j\)]
15: prev_cost = curr_cost.  prev_path = curr_path  final_min_index = index of minimum value of curr_cost  final_path = curr_path[final_min_index] return final_path.
```  
**Algorithm A1** Solving the cluster assignment
Since our method uses the DL models developed for image analysis, any improvements in anomalous image detection are easily adapted for anomalous network traffic detection. For example, the research on 2D-CNN usage is ongoing and plentiful due to their visual applications, so there are a wide variety of different models to use for analysis. In contrast, the research into network security has focused mainly on 1D-CNN and other types of machine learning (ML) models. By efficiently converting packets into a format suitable analysis by the models used in visual machine learning tasks, any innovation in the field of computer vision may potentially be applied to network security, as well. Furthermore, by using raw packet data instead of extracted features, our method allows for real-time traffic classification as soon as a new connection is initiated. In contrast, extraction of features requires collecting a minimum amount of network flow data before the analysis can begin. Therefore, our method uses fewer computational resources than any similar method that requires extracted features before applying a 2D-CNN model for analysis. Instead of storing and computing aggregate data, a necessary step for feature extraction, our method performs as few transformations on the raw packet data as possible before analyzing it. The binary data of a packet are simply appended to a predefined bitmap file header; no other changes need to be made. Therefore, our methodology will likely be superior to the current malicious traffic detection methods. It allows for real-time analysis with less preprocessing overhead or specialized configuration and focuses on useful information to identify malicious flows. There is also a unique potential for further refinement by repurposing any advancement in computer vision research for use in this methodology. The contributions we present in this paper are: (i) the proposal and demonstration of an efficient process for converting raw IP datagrams into bitmap images for analysis using DL models from computer vision research and (ii) a simple and effective deep learning model for computer vision capable of analyzing these images. We also (iii) test this method on several datasets. The first contribution is significant because it allows for accessible live malicious traffic detection using computer vision analysis. The second is an extension of the first that provides a practical deployment of the proposed network defense method. The third contribution demonstrates the effectiveness of this method and allows for comparison with other methods of live network defense. The rest of this paper is structured as follows: Section 2 describes background of this research and related work, Section 3 describes the concept and design of our raw network analysis method, Section 4 outlines the implementation of our design, Section 5 reports the results of our initial experiments with this implementation, Section 6 discusses those results and their merits, and Section 7 summarily concludes the paper.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Interference Across Heterogeneous and Multi-Domain Networks  
Most reviewed approaches focus on terrestrial IM. However, B5G networks are expected to incorporate NTNs, such as UAVs [37], high-altitude platforms [234], and low earth orbit satellites [235]. These additional layers introduce new interference dynamics (e.g., air-to-ground, satellite-to-terrestrial links) that are insufficiently addressed in current frameworks.
## 4 Synthesis and Summary
### Future Directions
#### 4.4.7 Brief Summary  
In summary, one of the primary areas of development will be the enhancement of intelligence and autonomy for USVs. This evolution is expected to be fueled by the integration of sophisticated AI, deep learning and machine learning technologies. These advancements will enable USVs to better perceive their environment, make autonomous decisions and execute complex tasks with greater efficiency and accuracy. The focus will also be on developing advanced sensing and obstacle avoidance systems, as well as robust decision support systems, which will further enhance their operational capabilities in diverse environments. The progression in autonomy will likely include more sophisticated features such as autonomous path planning, cruising and maintenance, thereby reducing the reliance on human operators and enhancing operational efficiency. Another significant area of development for USVs will be the integration of highly advanced sensor systems and the ability to execute multi-modal tasks. The advancement in sensor technology, including high-resolution cameras, LiDAR, sonar and radar, will significantly improve the environmental perception capabilities of USVs. This, in turn, will expand their applicability in various fields such as oceanographic research, marine conservation, military operations and emergency responses. Furthermore, the enhancement in endurance and resilience through innovative power systems and fault-tolerant designs will enable USVs to undertake longer-duration missions and operate effectively in challenging conditions. The integration of renewable energy sources and eco-friendly practices in USV design will also contribute to sustainable maritime operations. In addition, the increasing use of satellite communication and improved interconnectivity will enhance remote operation capabilities, allowing for more coordinated and collaborative efforts in maritime missions. Table 7 shows a brief and overall summary of this section. These developments, combined with the ongoing research in navigation safety and military defense applications, suggest that USVs will play an increasingly vital role in the future of maritime operations, offering both enhanced capabilities and cost-effective solutions.
###### Abstract  
Cooperative localization (CL) is a popular research topic in the area of localization. Research is becoming more focused on Unmanned Aerial Vehicles (UAVs) and robots and less on pedestrians. This is because UAVs and robots can work in formation, but pedestrians cannot. In this study, we develop an adaptive decentralized cooperative localization (DCL) algorithm for a group of firefighters. Every member maintains a local filter and estimates the position and the relative measurement noise covariance is estimated rather than a fixed value. We derived the explicit expressions for the inter-member collaboration instead of using approximations. This method reduces the influence of non-line-of-sight (NLOS) errors in the ultra-wideband (UWB) ranging on the CL, eliminating the need for fixed UWB anchors. The proposed algorithm was validated by two experiments designed in the building and forest environments. The experimental results demonstrate that the proposed algorithm improved the accuracy of localization, and the proposed algorithm suppressed the localization errors by 14.23% and 47.01% compared to the decentralized cooperative localization extended Kalman filter (DCLEKF) algorithm, respectively.  
inertial navigation; cooperative localization; adaptive extended Kalman filter; UWB ranging; multi-sensor fusion +
Footnote †: journal: Medical Imaging  
1
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 3 Geospatial Data Processing
### Geodetic GPS Data: Linkage to the Reference Frame  
In order to obtain precise positions of the geodetic network, the data were linked to the terrestrial reference frame IGS08 [43], providing consistency with the IGS products, such as satellite orbits, corrections to antenna phase centers, Earth orientation and rotation parameters, etc. Eight continuous GPS stations of the IGS global network were linked to the proposed geodetic network used in this study. These stations generate the regional network and are BRAZ, FALK, LPGS, OHI2, PLAM, PARC and UNSA (IGS Stations), and CHLT (IGN-Ar Station) (Figure 1). Data from these stations were downloaded in RINEX format from the Scripps Orbit and Permanent Array Center (SOPAC) repository ([[http://garner.ucsd.edu/](http://garner.ucsd.edu/)]([http://garner.ucsd.edu/](http://garner.ucsd.edu/)), accessed on 21 December 2021), except for the CHLT station, which belongs to the Argentine National Geographic Institute (IGN-Ar). Table 3 shows the stations used, as well as their location, geodetic coordinates, tectonic plate where they are located, and the agency to which they belong.
## 6 Discussion on Blast Overpressure Reduction
### Effect of Blast Pocket  
The maximum overpressures, \(P_{M}\), and overpressure reduction as a function of the blast-pocket ratios \(L_{b}/L_{w}\) are shown in Figure 21a,b, respectively. The maximum overpressures, \(P_{M}\), tend to increase with increasing the ratio, \(L_{b}/L_{w}\). For the considered ratios of the blast pocket, the smallest ratio, \(L_{b}/L_{w}\), of 0.5 led to the greatest reduction in the maximum overpressure, \(P_{M}\), compared to the original model for all cases. For \(L_{b}/L_{w}\) of 0.5 and \(\alpha_{s}\) = 90\({}^{\circ}\), \(P_{M}\) occurred at the second peak (approximately \(t\) = 0.26 s) whereas \(P_{M}\) for \(L_{b}/L_{w}\)  
Figure 20: Overpressure distributions for Model Arc-90-0 and Arc-60-05. (**a**) Model Arc-90-0 (original), \(t\) = 73 ms; (**b**) Model Arc-60-05, \(t\) = 77 ms; (**c**) Model Arc-90-0 (original), \(t\) = 158 ms; (**d**) Model Arc-60-05, \(t\) = 177 ms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Slope Angle of**} & \multicolumn{4}{c}{**Blast Overpressure Reduction (\%)**} \\ \cline{2-5}  & \(L_{b}/L_{w}\) = 0.5 & \(L_{b}/L_{w}\) = 1.0 & \(L_{b}/L_{w}\) = 1.5 & \(L_{b}/L_{w}\) = 2.0 \\ \hline
90\({}^{\circ}\) & 46.2 & 34.1 & 29.1 & 22.1 \\ \hline
75\({}^{\circ}\) & 57.7 & 36.4 & 27.3 & 30.9 \\ \hline
60\({}^{\circ}\) & 64.5 & 44.3 & 30.0 & 34.0 \\ \hline
45\({}^{\circ}\) & 60.9 & 37.9 & 48.3 & 31.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overpressure reduction varying the slope of external tunnel entrance.  
of 1.0, 1.5, and 2.0 was observed at the fourth, third, and third peaks, respectively, (\(t=0.42\). 0.35, 0.36 s), as shown in Figure 18a. This indicates that \(P_{M}\) for \(L_{b}/L_{w}\) of 0.5 was affected by blast waves near the shock front, but those for \(L_{b}/L_{w}\) of 1.0, 1.5, and 2.0 was by blast waves, following the shock front, that were reflected, diffracted, and amplified by the internal walls of the blast pockets. Similar observations were made for \(\alpha_{s}=75^{\circ}\), \(60^{\circ}\), and \(45^{\circ}\), as shown in Figure 18b-d.  
Figure 22 shows the overpressure distributions simulated for the original model (Arc-90-0) and Model Arc-60-05 at \(t=120\) ms. It is observed that for the identical ratio, \(L_{b}/L_{w}\), of 0.5, Model Arc-60-05 with an angle, \(\alpha_{s}\), of \(60^{\circ}\) resulted in slightly smaller overpressures at the front of the shock waves than the original model with the vertical tunnel entrance (\(\alpha_{s}=90\)).