esa-sceva/satcom-chunk-collection huggingface dataset:
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 3 Proposed Statistical Dimensioning Model
### Dimensioning Objectives in a Typical Cell  
In the typical cell illustrated in Figure 1, the collector o allocates according to the level of the received SINR, 1 to \(N_{\text{max}}\) RRs to the sensor node in order to achieve its target data rate \(C_{0}\). The required number of RRs is,  
\[N_{\text{RR}}(x,A_{f},I,P_{n})=\sum_{k=1}^{N_{\text{max}}}k\times\mathds{1} \big{\{}\operatorname*{\mathbb{SINR}}(x)\in[\gamma_{k}:\gamma_{k-1}]\big{\}}. \tag{8}\]  
with \(\gamma_{k}\) being the threshold SINR required to achieve a target rate of \(C_{0}/k\) within a single RR and \(\gamma_{0}>\gamma_{1}>\ldots>\gamma_{N_{\text{max}}}\) and \(\gamma_{0}\to\infty\). Note that \(\gamma_{0}\) (respectively \(\gamma_{N_{\text{max}}}\)) is the threshold SINR to decode the highest (respectively lowest) Modulation and Coding (MCS) scheme. If \(\operatorname*{\mathbb{SINR}}(0,x)<\gamma_{N_{\text{max}}}\), the sensor will not be able to decode the lowest MCS and the collector does not attribute any RR to this user. This event of having insufficient SINR occurs with probability, we intentionally do not refer to this probability as outage to avoid confusion with the network outage probability.  
\[\mathbb{P}_{\text{off}}=\text{Prob}\{\operatorname*{\mathbb{SINR}}(0,x)< \gamma_{N_{\text{max}}}\}. \tag{9}\]  
Figure 1: Network model: typical cell and active nodes.  
By setting  
\[A_{f,k}=\gamma_{k}\alpha^{-1}r^{\beta}(P_{n}+I),\qquad\forall 1\leq k\leq N_{ \max}, \tag{10}\]  
with \(A_{f,0}=\infty\) the threshold fading to achieve a rate \(C_{k}\in[C_{0}/k\;;\;C_{0}/(k-1)]\) over one RR, the number of RR can be rewritten as,  
\[N_{\text{RR}}(x,A_{f},I,P_{n})=\sum_{k=1}^{N_{\max}}k\times\mathds{1}_{\big{\{} A_{f,k}\leq A_{f}\leq A_{f,k-1}\big{\}}}. \tag{11}\]  
The total of required RR in this typical cell depicted in Figure 1 is,  
\[N_{RR,I}(0)=\sum_{x\in\Phi_{c}(y_{0})}N_{RR}(x,A_{f},I,P_{n}). \tag{12}\]  
The network is in outage if,  
\[\mathbb{P}_{\text{out},c}(N_{t})=\text{Prob}\{N_{RR,I}(0)>N_{t}\}. \tag{13}\]  
In order to ensure an optimized network dimensioning, the number of total radio resources \(N_{t}\) that ensure a network outage probability of \(p_{\text{th},n}\), should be found. Using the concentration inequality, the typical cell outage probability is upper-bounded by,  
\[\mathbb{P}_{\text{out},c}(N_{t})\leq\mathbb{P}_{\text{sup}}(N_{t}), \tag{14}\]  
where  
\[\mathbb{P}_{\text{sup}}(N_{t})=\exp\left(-\frac{v_{N}}{N_{\max}^{2}}g\left( \frac{N_{\max}(N_{t}-m_{N})}{v_{N}}\right)\right), \tag{15}\]  
with  
\[m_{N} = \mathbb{E}\Big{[}\sum_{x\in\Phi_{c}(o)}N_{RR}(x)\Big{]}, \tag{16}\] \[v_{N} = \mathbb{E}\Big{[}\sum_{x\in\Phi_{c}(o)}N_{RR}^{2}(x)\Big{]}, \tag{17}\]  
the function \(g(t)=(1+t)\log(1+t)-t\) and \(N_{t}>m_{N}\). By setting a threshold \(p_{th,n}\) on the network outage,  
\[N_{t} = m_{N}+\frac{v_{N}}{N_{\max}}g^{-1}\left(\frac{N_{\max}^{2}}{v_{ n}}\log\left(\frac{1}{p_{\text{th},n}}\right)\right).\]
# Crack Propagation Law of Reinforced Concrete Beams
## 5 Conclusions  
The variations in the strain, deflection, and crack propagation of reinforcement concrete under load are primarily compared and tested while considering the influence of mesh size and concrete constitution. Subsequently, the stress, strain, and concrete damage of both concrete and rebar at joints in practical engineering applications are analyzed, yielding the following results:  
1. Calculation of the nonlinear behavior of concrete based on the constitutive formula in GB 50010-2010 has been proven to be more appropriate. The obtained axial tensile/compressive stress-strain curves, as well as the axial tensile/compressive damage-strain curves of concrete, can more accurately simulate the concrete stresses in the tests;
2. The simply supported test beams using over-reinforced beams all experienced shear and compressive failure and the development of oblique cracks in reinforced concrete beams was studied. The strain changes of reinforcement during the failure process of test beams were qualitatively analyzed. The strain of the longitudinal bar at the bottom of the beam shows an approximately linear increase in both test and simulation results, with no bending failure observed in the test beam;
3. The Abaqus concrete damage plastic model was unable to quantitatively reflect the width, spacing, and depth of macroscopic cracks. However, it could qualitatively illustrate the development direction and propagation process of cracks through the DAMAGET diagram;
4. Reinforced concrete joints were modeled using Abaqus to predict their forces and crack development. The cracks in the joint between the frame and the floor slab mainly showed bending cracks, with less compressive damage and more tensile damage to the concrete in the core area of the joint. With the increase in load, the crack developed outward from near the end of the column to form a vertical bending crack and the depth and width of the crack decreased gradually. This indicates that the design is relatively safe and meets the requirements of the bearing capacity of the oblique section.  
Conceptualization, Z.F. and Y.Y.; Software, H.Y. and Y.Y.; Resources, Z.F.; Supervision, Y.Y. and Z.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities of China, grant number FRF-TP-22-117A1.  
Not applicable.  
All the data supporting the results were provided within the article.  
The authors declare no conflict of interest.  
Figure 19: Concrete damage distribution of the joint. (**a**) Concrete compression damage distribution; (**b**) Concrete tension damage distribution.
# A Speed Tracking Method for Autonomous Driving via ADRC with Extended State Observer
## 3 Longitudinal Speed Tracking Controller Design
### Model-Based Feedforward Controller Design  
This study developed a model-based feedforward controller (MFC) with acceleration preview to reduce the system response time. The MFC has three components: A velocity compensator, an acceleration preview module, and an inverse dynamic model-based controller.  
When an autonomous vehicle is traveling along a hilly road, the inclines cause additional gravity acceleration or resistance \(a_{\beta}\). We created a slope velocity compensation module to improve the response of the controller when the vehicle was traveling uphill and to avoid frequent braking when traveling downhill:  
\[\begin{array}{l}a_{\beta}=\pm g\sin\beta\\ \Delta v_{\beta}=\int T\cdot a_{\beta}\end{array} \tag{27}\]  
where \(T\) is the control cycle and \(\Delta v_{\beta}\) is the compensatory velocity based on the angle of inclination of the road. The compensatory velocity is positive when the vehicle is traveling downhill and negative when it is traveling uphill. The new vehicle set speed is:  
\[v_{aimnew}=v_{aim}-\Delta v_{\beta} \tag{28}\]  
The acceleration preview module transforms the set speed to the desired acceleration, which is then used to calculate the desired drive torque in the inverse dynamic model-based controller. A preview time \(t_{p}\) to obtain the desired acceleration is determined, and the preview speed \(v_{p}\) is the speed at time \(t_{p}\) after the current moment. Thus, the desired vehicle acceleration is:  
\[a_{des}=(v_{p}-v_{act})/t_{p} \tag{29}\]  
When desired vehicle acceleration \(a_{des}\) is positive, the inverse dynamic model-based controller will calculate the desired drive torque, and the desired wheel drive torque \(T_{w}\) is obtained:  
\[a_{des}=\frac{T_{w}-J_{w}\cdot\dot{\omega}_{h}}{mR_{w}}-\frac{F_{z}}{m} \tag{30}\]where \(I_{w}\) is the rotational inertia of vehicle wheel and \(\omega_{h}\) is the wheel angular velocity.  
The feedforward engine torque \(T_{i,ff}\) is:  
\[T_{i,ff}=\frac{R_{w}(m\cdot a_{des}+F_{z})+J_{w}\dot{\omega}_{h}}{i_{g}i_{0} \eta_{T}}+T_{fric,e}+J_{e}\dot{\omega}_{e} \tag{31}\]  
Thus, the throttle angle from the MFC is:  
\[\theta_{fb}=g^{-1}(T_{i,ff}) \tag{32}\]  
When the desired vehicle acceleration \(a_{des}\) is negative, the braking system produces brake torque to decelerate the vehicle:  
\[F_{brk}=-ma_{des}-F_{w}-F_{r}-F_{i} \tag{33}\]  
The parameters we need to calculate are obtained from the vehicle configuration, and the wheel speed is obtained from a sensor in the vehicle. The transmission gear is obtained from the control area network (CAN) bus in the vehicle in real-time, and vehicle mass and tire radius are obtained from the vehicle configuration parameters. The inaccuracies in the wheel and engine inertia are computed in the ESO as internal disturbances, and the inaccuracies in the MVEM (e.g., coefficient empirical value and model simplification) are also included as internal disturbances.
# Multimedia Vision for the Visually Impaired through 2D Multiarray Braille Display
## 3 Proposed eBook Reader Application for 2D Braille Display  
In recent years, various braille displays have been developed to improve the information accessibility for the VI. However, most of them are composed of braille cells in a single line and have the following typical disadvantages: (i) panning problem while reading an eBook; (ii) images or videos can be expressed only as text; and (iii) an assistant of the VI must know braille to sufficiently understand the multimedia contents. To address these problems, this study implemented an eBook reader application through a braille display that has multiarray braille cells. Therefore, to develop a more efficient and convenient braille device than the conventional devices, this study was jointly carried out by the Department of Computer Engineering of Gachon University and the assistive device development companies in Korea, namely Dot, and PCT. The braille display developed by the joint research team includes Braille OS, braille display simulator, and an eBook reader application, as shown in Figure 2. In this study, as shown in Figure 6, visual contents such as texts and images were expressed through a tablet having multiarray braille cells, while most computations related to the expression of visual/audio contents were operated in a smartphone. Thus, the issues discussed in Points (i) and (ii) above were addressed, and most of the text content, images, and videos were actually expressed. Moreover, through the interoperability between the braille display device and regular smartphones, the issue discussed in Point (iii) was also solved.  
Braille OS and braille pad simulator were the applications used by the respective devices, i.e., the tablet and smartphone. Braille OS was the OS used in the braille device, while braille pad was a simulator application for expressing the contents on the display. Braille OS has various built-in applications such as image viewer, calculator, games, and web viewer, in addition to an eBook reader application. Furthermore, this OS can be used in future Android-based braille devices. This study was performed by dividing the braille display into a simulator pad and a smartphone so that information could be exchanged through remote communication. In the future, the display and computation functions could possibly be combined in a standalone braille display, which could be separately linked with a smartphone. However, in this study, because the braille display has not yet been fully developed, the simulations were performed through a combination of a tablet and a smartphone.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 2 Notation and Preliminaries
### Evaluation Criteria of Sparse Time-Series Clustering  
We focus on shape-based clustering, where the clustering algorithm seeks to minimize \(\text{Cost}(\mathcal{X},k)\), with \(\mathcal{X}\) being a set of time series to be partitioned into a predefined number \(k\) of clusters. \(\text{Cost}(\mathcal{X},k)\) is given by (2), with respect to the \(\beta\)-DTW distance for every instance. To this end, we apply the \(k\)-means algorithm with the \(\beta\)-DTW distance for some fixed parameter \(\beta\geq 0\), and the DTW Barycenter Averaging (DBA) algorithm [19] for updating the cluster representatives in each iteration. For each dataset \(\mathcal{X}\), \(k\)-means is applied to the original time series in \(\mathcal{X}\) and to the time series consisting of the inducing points placed by Sparse Gaussian Process Regression (SGPR) applied to each time series in \(\mathcal{X}\).  
A ground truth clustering is available for all our instances \((\mathcal{X},k)\). Therefore, to evaluate the performance of our approach, we resort to the _Adjusted Rand Index_ (ARI), an extensively used extrinsic clustering metric quantifying how much the clustering computed by our algorithm overlaps with the ground truth clustering. For completeness, we briefly define the Rand Index (RI) and the Adjusted Rand Index (ARI) below.
## 2 System Model
### Problem Formulation  
In the given system model, our goal is to enable all USVs to autonomously navigate to their respective target areas by optimizing all trajectories, and each target is only assigned to one USV. Then, we have  
\[\left\{\begin{array}{l}\sum\limits_{i=1}^{K_{U}}T_{i}^{t}=\mathbf{T},i\in \left\{1,\ldots,K_{T}\right\}\\ \forall i\
eq j,T_{i}^{t}\
eq T_{j}^{t}\,i,j\in\left\{1,\ldots,K_{T}\right\} \end{array}\right. \tag{2}\]  
where \(\mathbf{T}\) represent all assignable regions. All USVs can navigate by adjusting their positions dynamically to avoid static and dynamic obstacles, which can be expressed as  
\[\forall i,j,O_{j}\
otin P_{i}\,i\in\left\{1,\ldots,N\right\},j\in\left\{1, \ldots,U_{K}\right\} \tag{3}\]  
Figure 1: Multi-USV communication system.  
During the mission, each USV needs to maintain a certain distance to stay within communication distance. We utilized \(Z\) to describe the communication graph of \(K\) USVs, where \(E\) is the edge set, defined as  
\[E=\big{\{}\big{(}U_{i},U_{j}\big{)}\big{|}d\big{(}r_{i},r_{j}\big{)}=p_{i}-p_{j} \leq\rho_{c}\big{\}} \tag{4}\]  
where \(\rho_{c}\) is the maximum communication distance. In other words, when the distance between two USVs is less than the maximum communication distance, there will be an undirected edge between them, and they can communicate with each other. Accordingly, each USV collaboratively adjusts its position and selects the target area based on the hazards of its environment. The collision-free path is then formulated as:  
\[\forall i,j,\Big{(}x_{i}^{LI}(t),y_{i}^{LI}(t)\Big{)}\
eq\Big{(}x_{j}^{LI}(t), y_{j}^{LI}(t)\Big{)} \tag{5}\]  
Thus, in this article, a multi-agent deep reinforcement learning-based trajectory design and area assignment algorithm are proposed.
# Relaxed Data Types as Consistency Conditions +
## 8 Conclusions and Future Work  
In exploring the relation between relaxations for abstract data types and consistency conditions, we have shown that, in several cases, the ideas in each may be expressed equivalently by the other.  
Specifically, we showed that the \(k\)-Out-of-Order, \(k\)-Lateness, and \(k\)-Stuttering relaxations may be equivalently expressed as consistency conditions and that the consistency condition \(k\)-Atomicity can be equivalently expressed as a relaxation. For each of these, we define the equivalent consistency condition or relaxation. We then explore how the newly-defined consistency conditions fit into the space of consistency conditions, related by the conditions' strength, by showing that they are distinct from several previously-known similar conditions.  
In the future, we need to define or quantify the spaces of possible data type relaxations and consistency conditions. This would allow more general conclusions about the relation of the two fields. For example, it seems that every data type relaxation can be expressed as a consistency condition, while only some consistency conditions can be expressed as relaxations. If we could define the space of possible relaxations, we could formally show that the space of relaxations would be a subset of the space of consistency conditions. There is also the question of relaxing tasks and other distributed problems and data operations which cannot be sequentially specified. Such relaxations are not yet defined, but could lead to broader equivalences with consistency conditions.  
In this paper, we did not consider relaxing particular operations in a data type. It is possible, and common in the literature [6; 10], to relax the behavior of certain operations, while requiring that others behave as in the base type. In the case of per-operation relaxations, our result in Section 6 regarding data types where all mutators are overwriters would extend to all data types where all overwriting operations were \(k\)-Stuttering relaxed, greatly increasing their scope.  
Finally, it is not obvious how the complexity of implementations of shared data types would depend on which of the two approaches we use. Past work has improved efficiency by relaxing data types, so this work may enable us to more easily compare the complexity of consistency conditions, known and new, perhaps enabling us to distinguish incomparable conditions by performance.  
[PERSON] and [PERSON] developed the research questions and content together. [PERSON] led the writing effort, with both authors involved in final preparation.  
This work was partially supported by NSF Grant 1526725. We would like to thank the editors and anonymous reviewers for their insightful and detailed suggestions and feedback.  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 5 Existing Cybersecurity Solutions for Smart Manufacturing
### Other Security Solutions
#### 5.5.3 Digital Twin-Based Solutions  
A digital twin system is a real-time synchronized clone of the actual infrastructure. In the scenario of the manufacturing industry, a digital twin is a virtualized representation of the physical manufacturing objects that provides a possibility to visualize, monitor, and predict the state of such systems. The synchronization of the updates between the cyber-physical system throughout its life cycle and its digital twin is provided by a digital thread. The purpose of building a digital twin is to simulate a safe breach to analyze the system behavior under attack and estimate the potential damage. The result of this analysis will facilitate the security and safety design mechanism to produce a more robust and fault-tolerant architecture [21]. Digital twin systems can be used for penetration testing to identify vulnerabilities in the infrastructure and any unnecessary functionality of a device can be revealed. They also provide testbeds for security tests to be done virtually and fix vulnerabilities without impacting the real systems.  
Despite all the security challenges addressed by the digital twin architecture, they also pose a threat that allows attackers to exploit the digital twin systems to launch an attack on the actual production systems. Once an attacker breaches the perimeter of digital twin infrastructure, even though there is no impact on the real systems, the attacker can gain knowledge of the network and system configurations as they are replicas of the underlying cyber-physical systems. The digital thread, which is the link between the original system and its digital twin is an attractive target for the attackers to either gain access to the manufacturing environment or manipulate the synchronization data to divert the system into an insecure state [25].
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 6 Conclusions  
Interference during transmissions that utilize the same time-frequency wireless resources is one of the main issues in cellular networks. The interference issues have become more critical due to the continuous growth in traffic, density, and size of wireless networks, especially in B5G. Recently, new communication techniques have been developed to deal with interference, along with progress in the characterization of network limitations, which has improved our understanding and ability to enhance performance. This paper presents a comprehensive review of the IM issues in B5G cellular communications and highlights the significance of addressing interference in D2D communication, HetNets, ICI, and AI-based frameworks. Promising solutions have been extensively discussed, with a focus on the methodology, strengths and limitations, and open research challenges. Furthermore, the paper presents and analyzes the standardized IMI schemes proposed by the 3 GPP. As a literature review, this study provides a comprehensive analysis of current research trends, approaches, and directions in B5G IM. The goal is to guide researchers by summarizing validated results and identifying areas that warrant further theoretical or simulation-based investigation. Key insights include the growing role of AI-based coordination, the shift toward distributed and predictive IM strategies, and the increasing complexity of managing interference across heterogeneous and NTNs. Challenges such as scalability, CO, and AI transparency remain open research problems. In addition, a comparative perspective on optimization priorities across IM domains reveals that while objectives like SE and power consumption have received significant attention, aspects such as outage minimization and EE remain underexplored. By addressing these gaps, future studies can advance toward building more adaptive, secure, and intelligent IMI frameworks. In conclusion, effective IMI, particularly through 3 GPP-proposed techniques, represents a key step toward the successful deployment of future B5G wireless networks.  
Conceptualization: K.D., K.A.N. and S.A.; Methodology: O.T.H.A. and M.N.H.; Visualization: K.D. and K.A.N.; Writing--original draft preparation: O.T.H.A. and M.N.H.; Writing--review and editing: K.D. and S.A.; Funding acquisition: K.D. and K.A.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Grant Scheme (FRGS), Ministry of Higher Education (MoHE), Malaysia, under Grant FRGS/1/2020/TK0/UM/01/2; and in part by United Arab Emirates University UAEU-UM under Grant (IF090-2024).  
Not applicable.  
The authors would also like to thank the respected Editor and Reviewer for their support.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3G & third generation \\
3 GPP & third-generation partnership project \\
4G & fourth generation \\
5G & fifth generation \\
6G & sixth generation \\ \end{tabular}