esa-sceva/satcom-chunk-collection huggingface dataset:
### Motivations  
IoT seeks to link equipment to the web to make it accessible anytime, anywhere, and by anyone. With the help of seamless connectivity and smart objects like washing machines, microwave ovens, meters, vehicles, mobile phones, refrigerators, medical devices, etc., IoT is making remarkable applications such as intelligent transport systems, smart healthcare, smart homes, smart cities, etc. Ericsson forecasted that by 2022 there would be 29 billion connected devices, among which 18 billion will be IoT devices. Considering the vast utilization of IoT devices, the application of IoT and security requirements have increased. Moreover, many devices will be placed in rural areas which will be unattended. An attacker can compromise the devices and find an entry point to compromise the network. Research interest in IoT areas is increased a lot and it has become one of the first priorities among  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Notations** & **Description** & **Notations** & **Description** & **Notations** & **Description** \\ \hline IP & Internet Protocol & IoMT & Internet of Medical Things & IoV & Internet of Vehicles \\ \hline SG & Smart grid & PUF & Physical unclonable function & V2X & Vehicle-to-everything \\ \hline V2V & Vehicle-to-vehicle & VANET & Vehicular Adhoc Network & V2S & Vehicle-to-sensors \\ \hline V2I & Vehicle-to-infrastructure & V2N & Vehicle-to-network & V2P & Vehicle-to-pedestrian \\ \hline RFID & Radio-frequency identification & WiFi & Wireless Fidelity & NFC & Near-field communication \\ \hline GPS & Global Positioning System & LTE & Long-Term Evolution & MAC & Media Access Control \\ \hline M2M & Machine to machine & M2H & Machine to human & H2H & Human to human \\ \hline DoS & Denial-of-service & DDoS & Distributed denial of service & BFA & Brute force attack \\ \hline PUF & Physical unclonable functions & PKI & Public Key Infrastructure & AES & Advanced encryption standard \\ \hline ABE & Attribute Based & MAC & Message Authentication Code & ECC & Elliptic Curve Cryptography \\ \hline RSA & Rivest-Shamir–Adleman & PoW & Proof of work & PoS & Proof-of-Stake \\ \hline \hline \end{tabular}
\end{table}
Table 1: Acronyms used in the paper.  
Figure 1: Paper Organization.  
industry and academia, which can be found in the existing literature. Many survey papers show a glimpse or partial of IoT security threats or solutions. We have reviewed many existing studies related to IoT applications, different attacks, and security mechanisms. This paper aims to show security vulnerabilities in IoT devices as well as IoT networks with security solutions to provide insights to the network designers and motivate future research directions for both new and present researchers. The motivation of the paper is to provide all relevant information from existing literature under an umbrella that can benefit future readers by getting the required information from an article.
## 4 Proposed Path Planning Techniques
### Probabilistic Roadmaps (PRM) Algorithm  
The PRM algorithm is primarily used for multi-query applications. It consists of two main phases:  
1. Preprocessing Phase: In this phase, a graph (road map) noted (G) is generated. It is accessible from every point of the free configuration space \(C_{free}\);
2. Query Phase: The two points \(q_{s}\) (start point) and \(q_{G}\) (final point) are given. The sequence of edges that forms the required path is obtained from \(q_{s}\) to \(q_{G}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component Name** & **Specifications** & **Number** \\ \hline \multirow{6}{*}{Lidar sensor} & 2D laser scanner & \\  & Scan angle: 360 deg & \\  & Range: 12 m & \\  & Sampling points: 1450 points/scan & \\  & Frequency: 10 Hz & \\ \hline Magnetic encoder & Resolution: 360 step/deg & 4 \\ \hline \multirow{2}{*}{Inertial Measurement Unit (IMU)} & MPU9250 & \\  & 9-axis IMU & 1 \\ \hline Microcontroller & Arduino Mega 2560 & 2 \\ \hline \multirow{2}{*}{Master controller (PC)} & Processor: i5-9500 CPU & \\  & Ram: 8 GB & 1 \\ \hline Wireless module & APC 220 kit RF & 1 \\ \hline \multirow{4}{*}{Omni wheels} & Diameter: 58 mm & \\  & Roller: 13 mm & 4 \\ \cline{1-1}  & Material: Nylon & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main hardware components of the omni-wheeled mobile robot (4 OWMR).  
In the preprocessing phase, the generation of a road map is the main task of the preprocessing phase. The input is the number of iterations, and the number of nearest neighbors and the output is PRM G. G (V, E) represents a topological graph in which V is a set of vertices and E is the set of paths that map into \(C_{free}\).  
The algorithm initializes the vertices set and edges set by the empty set. In each iteration, while the absolute number of vertices is less than the number of samples, the algorithm selects a random point \(q\) from configuration space. Then it checks whether \(q\in C_{free}\) or not (line 4). Once \(q\in C_{free}\), it is inserted as a vertex of G. The pseudocode for this algorithm is shown in the following pseudocode.  
```
1:Initialize V = \(\emptyset\), E = \(\emptyset\)
2:while\(\mid\)V \(\mid\) < n do // find n collision-free points qi
3:\(\quad\) q \(\leftarrow\) random sample from C
4:if\(\text{q}\in C_{\text{free}}\)then V \(\leftarrow\) V \(\cup\) {q}
5:endwhile
6:for all q \(\in\) V do // check if near points can be connected
7:\(\quad\) Nq \(\leftarrow\) k nearest neighbors of q in V
8:for all q\({}^{\prime}\in\) Nq do
9:if path (q, q\({}^{\prime}\)) \(\in\) C\({}_{\text{free}}\)then E \(\leftarrow\) E \(\cup\) {(q, q\({}^{\prime}\))}
10:endfor
11:endfor
```  
**Algorithm 1** The  
The next step is to try to connect q to some nearby vertices G. The algorithm checks the path from q to q' to see if it can be constructed or not; if yes, the set of edges will be increased by a new edge [35].
## 4 Influence of 3D Printing Parameters on the Fatigue Life of FFF Prints
### Printing Speed  
Printing speed refers to the movement speed of the extruder. Higher printing speeds are desired when the printing time is of paramount significance. However, high printing speeds usually come at the cost of lower surface quality and reduced static strength [88]. In a study by [PERSON] et al. [69], the tensile fatigue behaviour of ABS prints at two different speeds (2000 and 4000 mm/min) was investigated. The authors reported that the feed rate is of significant influence, showing an inverse relation with fatigue life. That is, the samples printed at the lower printing speed (2000 mm/min) exhibited a higher fatigue life. However, printing polymers at low speeds causes the filaments to be in contact with the hot nozzle for a longer time, increasing the temperature within the filament. When exposed to elevated temperatures, thermal degradation occurs in polymers, resulting in degraded mechanical properties [89]. This should be considered as a limitation for when the optimum printing speed is to be determined.  
According to previous studies, the impact of the printing speed in the case of rotating bending fatigue is not as influential as shown in [69]. ABS cylindrical samples were 3D-printed at different printing speeds (25, 30, and 35 mm/s), and their rotating bending fatigue behaviour was analyzed [64; 67]. It was shown that the impact of the printing speed on fatigue life is insignificant. The same result was achieved for PLA prints [66] and PLA-wood composite prints [18] subjected to rotating bending fatigue. However, it is worth noting that the number of published results and the range of velocities studied in the literature are insufficient to draw a conclusion. Thus, further investigations should be conducted focusing on the bonding strength, the interfacial characteristics of extruded filaments' junctions, and the fatigue behaviour of FFF prints when different printing speeds are used.
# Determination of Cycle to Cycle Battery Cell Degradation with High-Precision Measurements
## 4 Conclusions  
In this paper, it was shown how high-precision capacity measurements of Lithium ion cells can be used to determine cell aging from cycle to cycle. Initially, the high temperature stability, high drift stability and low noise of the measurement system was demonstrated, suggesting that it is possible to determine even the smallest capacity changes between individual cycles. Subsequently, the cycle aging, calendrical aging and leakage charge of LG INR18650 MJ1 cells are determined for different cycling schemes. The main difference between the cycling schemes is that the cycling current (C/4, C/12 and C/36) and the average SoC (50%, 100%) are varied. For this particular cell, cycle aging is dominant in all cases and calendrical aging and leakage charge only become problematic at a high state of charges. Furthermore, it was shown that, by observing the cell from cycle to cycle, it is recognized early on that the aging rates vary significantly over cycles and time.  
Conceptualization, D.S., B.S. and H.W.; methodology, D.S. and B.S.; writing--original draft preparation, D.S.; writing--review and editing, B.S., M.N. and H.W.; visualization, D.S.; supervision, H.W.; project administration, H.W.; funding acquisition, H.W., B.S. and M.N. All authors have read and agreed to the published version of the manuscript.  
The financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, and the Christian Doppler Research Association are gratefully acknowledged.  
Not applicable.  
Open Access Funding by the Graz University of Technology.  
The authors declare no conflict of interest.
## 3 Results  
If the aforementioned calculation were performed by ignoring the table of differences and using the (original, not modified) Krovak projection, only the corrections to the lengths of the Krovak projection would be presented in the results. The corresponding result is continuous smooth lines; see Figure 5 in [11]. By subtracting these results from the global length correction, i.e., the length correction from the projection and from the deformation, we obtain the length correction from the deformation only. Using the procedure described in Section 2, the resulting length corrections from the S-JTSK projection and deformation, i.e., the global length corrections, are shown graphically in Figures 6 and 7. It can be seen that after adding the S-JTSK deformation (from the table of differences and using the modified Krovak projection) to the length correction from the (original)  
Figure 4: Definition of segments around node [32]. Krovak projection, the smooth lines (see Figure 5) become \"twisted isochores\"; see Figure 8 in the axonometric view. Because it is possible to study the influence of the deformation of the S-JTSK reference system, in the following paragraphs, we do not use the global length corrections; instead, we use only the aforementioned length correction from the deformation. The numerical statistics of the length correction from only the deformations of all nodes with a 2 km step are summarized in Table 1. In Table 2, the frequencies of the length correction values in each interval are shown. Figure 5: Length distortion in Krovák projection in cm/km [11]. Figure 6: The global length corrections, i.e., the length corrections from the projection and the deformation. A color representation of the correction values at nodes in the 2 km grid [32]. Figure 8: An image (”washbasin”) of the global length corrections, i.e., the length correction from the projection and deformation in cm/km [32]. \begin{table}
\begin{tabular}{c c} \hline Average & 0.36 \\ Minimum & \(-\)1.63 \\ Maximum & +2.50 \\ \hline \end{tabular}
\end{table} 
Table 1: Deformation length correction statistics in cm/km. Figure 7: The isolines of the global length corrections, i.e., the length correction from the projection and deformation in cm/km [32]. Figure 9 shows the frequencies from Table 2. The results show that 99% of the length correction values from the coordinate system deformations lie in the interval of \(-1.4\) to \(+1.4\) cm/km. However, the mean value is not zero; it is approximately \(+0.3\) cm/km. This result is expected from the statistics presented in Table 1 and indicates a residual change in the scale of the S-JTSK reference system relative to the ETRF2000 reference frame, which was not removed within the seven-parameter Helmert similarity transformation. However, it corresponds to the accuracy of the S-JTSK implementation.
## 5 SSFXG Architecture and Basic Interactions
### NW Service Provisioning
#### 5.2.3 Attach NW Subscriber to the NW After Being Verified  
At this final stage of the process, the NW provider retrieves the \(DIDDoc_{SUB}\) associated with the subscriber's \(DID_{SUB}\) from the blockchain ledger (Step 6 Figure 3). This document contains all the public details related to the subscriber, including their NW identity subscription number. Using the subscription number, the NW provider computes its hash value and compares it with the \(HASHID\) received earlier from the subscriber during the verification phase.  
If the computed hash value matches the \(HASHID\), it serves as proof that the subscriber's identity and credentials are valid and consistent with the records stored in the blockchain. This verification process ensures that the subscriber has a legitimate and globally recognized subscription within the federated NW.  
Once the validation is complete, the NW provider formally confirms the subscriber's attachment to the NW. This confirmation signifies that the subscriber is authorized to access and utilize the NW services under the terms agreed upon in their subscription (Step 7 Figure 3). Additionally, the NW provider sets up mechanisms to frequently update the subscriber about their billing and settlement status. This includes providing transparent information on service usage, billing cycles, and payment updates, ensuring the subscriber is informed and their NW access remains uninterrupted (Step 8 Figure 3).  
This final step concludes the onboarding and verification process, establishing a secure, transparent, and trusted connection between the subscriber and the NW provider. By utilizing blockchain technology and cryptographic verification, the NW guarantees the integrity and authenticity of subscriber credentials, enabling seamless service provisioning and maintaining privacy and security standards.
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 3 Deep-Space Telemetry
### In Search of the Missing 2 dB--Low-Density Parity Check (LDPC) Codes  
In contrast to the fast and efficient process for developing a turbo code standard, the development of an LDPC coding standard for CCSDS has been more difficult. This is because the standards process faces a number of important challenges. First, because LDPC codes are defined by a sparse parity check matrix with large dimensions, there are a large number of possible codes. Second, if no structure other than sparseness is imposed on the code design, the code cannot be described by a small number of parameters whose values can be optimized in the way it is done for turbo codes. Third, there is a long list of desirable attributes [95], and no LDPC code is superior in all categories [77].  
The CCSDS standards process considered the irregular repeat-accumulate (IRA) LDPC code in the second generation Digital Video Broadcast standard (DVB-S2) [96], other irregular LDPC codes, and regular LDPC codes [77]. In August 2011, CCSDS adopted a family of nine LDPC codes proposed by JPL in the 131 Blue Book [50].  
The CCSDS LDPC codes are derived from a family of codes known as Accumulate Repeat-4 Jagged Accumulate (AR4 JA) where the name derives from the protograph structure used to define the code [77]. The nine LDPC codes are binary \((n,k)\) block codes. The family comprises two values of \(k\) and three values for \(n\) for each value of \(k\). The values are summarized in Table 2. The interesting feature of these codes is that the rate is changed by changing the code length \(n\) rather than its dimension \(k\). This provision was made to make the interface to telemetry packets transparent to the code rate.  
An interesting application of CCSDS LDPC codes is the Mars Relay Network. The Mars Relay Network comprises a series of satellites in orbit around Mars (as of 2018, there were five operational options [97]) that receive data from \"rovers\" on the Martian surface and re-transmit that data to Earth [98]. The Mars Science Laboratory (or _Curiosity_ rover) has the capability to transmit its measurements directly to Earth or via the Mars Relay Network. Direct transmissions to Earth use the CCSDS rate-1/6 turbo code and an X-band carrier [99]. The Mars-to-orbiter portion of the Mars Relay Network uses LDPC codes [100; 101] using the CCSDS Proximity-1 space link protocol [102; 103] and a UHF carrier. The majority of the data transfer from Martian \"rovers\" to Earth occurs via the Mars Relay Network [98; 104].
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 5 Optimization on the ABE Scheme  
For the ABE scheme that is presented in Section 4, a plaintext has to be encrypted n times, where n is the size of the access structure. Therefore, it may be inefficient when the length of the plaintext or the size of the access structure is large. We further develop a hybrid encryption scheme, which uses the proposed ABE scheme for key encapsulation and the SM4 block cipher for data encapsulation to optimize the scheme.
## 3 Results and Discussion
### Voltage Distribution Simulation of Insulators and Insulator Strings
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Air** & **Ceramic** & **Cement** & **Steel** \\ \hline Relative dielectric constant & \(1\) & \(6\) & \(14\) & \(1\) \\ Electrical conductivity & \(1\times 10^{-4}\) & \(1\times 10^{-8}\) & \(1\times 10^{-9}\) & \(1\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters of insulator. Figure 2: Simulation of electric field distributions in insulators. (**a**) Constructed insulator structure for simulation. (**b**) Electrical potential distributions of insulator with effective insulation performance. (**c**) Electrical potential distributions of zero-value insulator. zero-value defects cause the insulation resistance of the insulator to drop sharply and lose its insulation property. The finite element simulations reveal a fundamental behavior in potential distribution: healthy insulators maintain substantial voltage gradients across their surfaces, while zero-value insulators exhibit near-equipotential conditions (<100 V) due to their collapsed impedance. This critical finding directly inspired our EL coating design strategy--by the application of the luminescent material at locations of maximum potential difference in functional insulators, the coating's light emission becomes a direct visual indicator of insulation integrity.
## 2 Method
### More Efficient Prototype Mask Branch: ProtoC1  
The prototype mask branch and mask coefficients are introduced to make the one-stage object detection model into a one-stage instance segmentation model. In the baseline model, the prototype mask branch is implemented with a fully convolutional network (FCN), which include one upsampling module and three Conv modules, in which there are two 2D convolutions with kernel size 3 and one 2D convolution with kernel size 1, named Proto. The input feature map is scaled up to twice its original size, and a feature map with k channel is outputted.  
Although the addition of the prototype mask branch makes the one-stage object detection model become a segmentation model, it also makes the detection speed decrease and the model volume increase. In the LCA-YOLOv8-seg model, we redesign the prototype mask branch.  
According to the implementation of convolution, the larger the kernel size of convolution is, the smoother the image will be, but at the same time, the calculation of convolution will increase. The same applies to the generation of an instance mask. In an experimental study, we found that using a 2D convolution with kernel size 1, while significantly reducing the parameters and calculations of the prototype mask branch, resulted in a big loss of edge detail and accuracy in the instance mask. The use of 2D convolution with kernel size 3 does not result in a big loss of mask detail, but the parameters and calculation of the branch is bigger than the branch using 2D convolution with kernel size 1. In order to strike  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Module** & **Input** & **Output** & **Mid** & **k** & **s** & **NL** \\ \hline Conv & 3 & 8 & - & 3 & 2 & RE \\ dblock & 8 & 8 & 12 & 3 & 2 & RE \\ dblock & 8 & 12 & 54 & 3 & 2 & RE \\ dblock & 12 & 12 & 66 & 3 & 1 & RE \\ dblock & 12 & 24 & 72 & 5 & 2 & RE \\ dblock & 24 & 24 & 180 & 5 & 1 & RE \\ dblock & 24 & 24 & 90 & 5 & 1 & RE \\ dblock & 24 & 24 & 108 & 5 & 1 & RE \\ dblock & 24 & 48 & 216 & 5 & 2 & RE \\ dblock & 48 & 48 & 432 & 5 & 1 & RE \\ dblock & 48 & 48 & 432 & 5 & 1 & RE \\ \hline \a balance between the calculation cost of the prototype mask branch and the quality of the instance mask, we proposed a new prototype mask branch structure, named ProtoC1, only including an upsampling module and a Conv module with one 2D convolution with kernel size 3. The new prototype mask branch ProtoC1 keeps the quality of the instance masks the same as the original branch, significantly reduces the parameters and complexity in the prototype mask branch and speeds up the processing speed of the prototype branch. The specific structure of Proto and ProtoC1 is shown in Figure 7.
## 1 Introduction
The methodology consists of three phases: (i) concept extraction--semantically annotating concepts present within the micro-contributions with ontological entities; (ii) concept consolidation--identifying and grouping concepts representing the same ontological entity; and (iii) profile creation--building short-term expertise profiles (_i.e._, restricted by a time window--two weeks, one month, _etc._) and long-term expertise profiles--as aggregations of short-term profiles. Initially, we applied the methodology in the biomedical domain, mainly because of the existing tool support. In particular, the concept extraction phase relied on the _NCBO Annotator_[8], hence making the accuracy of the generated profiles dependent on the accuracy of this tool. Nevertheless, experimental results demonstrated that our approach is much better suited for handling evolving knowledge than the existing traditional techniques. In this paper, we build on our previous work and we investigate the effect of using diverse _Language Modeling_ techniques in the concept extraction phase, in order to minimize the role of any domain-specific annotation tool. More concretely, this paper brings the following contributions:  
* we integrate two _Statistical Language Modeling_ mechanisms into the STEP methodology (_i.e._, Topic models and N-Gram models);
* we evaluate the effect of these models on the resulting expertise profiles by comparing them against the original results;
* we introduce the _Profile Explorer_, a visualization tool that provides a novel paradigm for _browsing_ and _analysing time-dependent_ expertise and interests which _evolve_ over time. The remainder of the paper is structured as follows. In Sections 2 and 3 we discuss the motivation and use cases associated with our research. Section 4 revisits the Semantic and Time-dependent Expertise Profiling (STEP) methodology, while Section 5 introduces the application of language modeling techniques as part of the concept extraction and profile building phases of STEP. Section 6 presents a comparative overview of the experimental results achieved by applying the new language modeling techniques, followed by a discussion of these results and of the limitations of our approach in Section 7. In Section 8 we introduce the \"Profile Explorer\" visualization paradigm, and before concluding in Section 10, we provide a comprehensive overview of the related work in Section 9.