esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks
## 5 Numerical Results and Analysis
### Optimal Values of \(\alpha_{1}\) and \(\alpha_{2}\)  
To find optimal values of \(\alpha_{1}\) and \(\alpha_{2}\), two optimizers, Adgrad and RMSProp, were used. Table 6 shows optimal parameters \((\alpha_{1}^{*},\alpha_{2}^{*})\) in the case of 5G CDL-A SISO, whereas Table 7 shows optimal parameters \((\alpha_{1}^{*},\alpha_{2}^{*})\) in the case of 5G TDL-A 2 \(\times\) 2 MIMO. The MSE performances of both optimizers were similar. In our previous work [12], we analyzed the optimal EESM parameters in 4G LTE-A system. At that time, the MSE performance of RMSProp was better than that of AdaGrad. However, since 5G NR AWGN curve aws too steep, there was little difference in performance of the two optimizers. In the case of 2 \(\times\) 2 MIMO, the variance of \(sint_{1}\cdots sint_{N_{RE}}\) was larger than that of SISO, thus the MSE of MIMO was larger than MSE of SISO.
## 4 Experiment Results and Analysis  
In order to evaluate the performance of the proposed saliency cuts method, we conducted experiments using the C++ language and a PC with 3.60 GHz CPU and 4 GB of RAM. We used an MSRA 10k dataset [17], which included 10,000 natural scene images and human manually labelled ground truth, which is exact and full salient object(s) in the given image. To the best of our knowledge, the database is the largest of its kind, and has pixel-level ground truth in the form of accurate human-marked labels for salient regions. We selected 6000 natural scene images with its corresponding ground truth images that have various types of single and multiple objects from MSRA 10K dataset for efficient evaluation. We performed qualitative and quantitative comparisons between the proposed method and other techniques, and also subjectively evaluated the proposed algorithm.
# Improving the Solving of Optimization Problems:
## 1 Introduction  
Solving optimization problems is a fundamental challenge across various research and industrial domains, including finance [1], automatic control systems [2], machine learning [3], and telecommunications [4].  
Many optimization problems belong to the NP (Nondeterministic Polynomial time) complexity class, which includes decision problems that, although not solvable in polynomial time, can be verified in polynomial time using classical strategies [5]. Particularly, NP-complete problems are significant since every problem in NP can be reduced to them in polynomial time. Solving any NP-complete problem in polynomial time would imply \(\mathrm{P}=\mathrm{NP}\). In contrast, NP-hard problems are at least as difficult as NP-complete problems but may not be verifiable in polynomial time and are not limited to decision problems, encompassing many optimization tasks.  
The growing need for efficient solutions to large-scale problems has driven continuous innovation, fostering new approaches that better explore solution spaces [6].  
With advances in quantum computing [7], interest has risen in evaluating its potential for optimization. Quantum computing could reduce computational complexity andimprove solution quality, addressing challenges in diverse fields such as healthcare, energy systems, and finance. Notable examples include grid management [8] and portfolio optimization [9]. By enhancing optimization techniques, quantum approaches can bridge the gap between computational methods and real-world applications.  
Currently, two main methods are employed: quantum annealers (QAs) [10, 11], special-purpose devices for optimization tasks, and algorithms based on quantum circuits or quantum gate arrays (QGAs) [7], which run on general-purpose quantum hardware or classical simulators. To leverage quantum solvers, optimization problems must be reformulated into quantum-compliant models. The Ising [12] and Quadratic Unconstrained Binary Optimization (QUBO) [13] models are widely used for this purpose. However, translating problems into these formulations often demands specialized expertise. To simplify this process, recent years have seen the development of tools and frameworks to assist users.  
This review provides a comprehensive overview of quantum approaches to optimization, emphasizing their advantages, challenges, and limitations. It demonstrates how these methods apply to real-world scenarios, detailing the required steps and offering practical examples. Furthermore, it examines tools, frameworks, and solvers that support users in adopting quantum techniques for optimization tasks.  
The article is organized as follows: Section 2 reviews optimization problems and conventional solvers. Section 3 discusses quantum-compliant formulations and tools for problem translation. Sections 4 and 5 focus on quantum annealers and circuit-based solvers, respectively. Finally, Section 6 concludes the article.
## 3 The Spatial Finite Helical Motion for Compound Transmission
### Basic Kinematic Relation  
In order to facilitate the study of the curve-face gear compound transmission, the right-handed Cartesian coordinate systems as shown in Figure 6 are established.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types** & **Case I** & **Case II** & **Case III** \\ \hline \multirow{3}{*}{Models} & \multirow{3}{*}{\(\begin{array}{c}\text{Kinds}\\ \text{Input}\\ \end{array}\)} & \multirow{3}{*}{\(\begin{array}{c}\text{Kinds}\\ \text{Input}\\ \end{array}\)} & \multirow{3}{*}{\(\begin{array}{c}\text{Moving-axis rotation}\\ \text{Fixed-axis rotation}\\ \end{array}\)} \\ \cline{1-  
The transmission ratio of curve-face gear compound transmission in this paper is a scalar and defined as \(i(\theta_{1})=\omega_{2}/\omega_{1}\).  
And then, the angular displacement \(\theta_{2}\) of the curve-face gear is:  
\[\theta_{2}(\theta_{1})=\int_{0}^{\theta_{1}}i(\theta)d\theta \tag{11}\]  
The axial displacement \(l(\theta_{1})\) of the curve-face gear is:  
\[l(\theta_{1})=\int_{0}^{\theta_{1}}\sqrt{r^{2}-(R\cdot i)^{2}}d\theta+R\cdot i (\theta_{1})-r \tag{12}\]  
Further, axial velocity \(v_{0}(\theta_{1})\) of the curve-face gear can be calculated as:  
\[v_{0}(\theta_{1})=\frac{dl(\theta_{1})}{dt}=\omega_{1}\sqrt{r^{2}-\left[R \cdot i(\theta_{1})\right]^{2}}+\omega_{1}R\cdot i^{\prime}(\theta_{1}) \tag{13}\]  
The basic kinematic relation of the curve-face gear compound transmission is shown in Figure 7, including the output angular displacement \(\theta_{2}\), the transmission ratio \(i(\theta_{1})\), and the axial displacement \(l(\theta_{1})\) and velocity \(v_{0}(\theta_{1})\) of the curve-face gear, which takes the input angular displacement \(\theta_{1}\) as the independent variable.  
Figure 7: The basic kinematic relation of the curve-face gear compound transmission.
# Evaluation of Adjustment Effects of Highway Guide Signs Based on the TOPSIS Method
## 4 Construction of Evaluation Index System
### Demand Analysis  
Highways must meet three core requirements from the drivers' perspective: safety, efficiency, and comfort. Guide signs, as essential tools for conveying information, play a pivotal role in fulfilling these objectives. To ensure optimal performance, several key factors must be considered: (1) Appropriate location: Signs should be placed where drivers can easily spot them. (2) Clear information: The design must allow quick reading and comprehension. (3) Continuity and consistency: Information should flow logically across signs to prevent confusion and ensure smooth navigation. Thus, for highways to deliver safe, efficient, and comfortable journeys, guide signs must exhibit standardization, readability, continuity, and consistency.  
Standardization: The proper placement and arrangement of guide signs directly affect the use efficiency of a highway. A well-designed layout can enhance driver comfort by providing effective information at the right moment, preventing confusion due to missing or improperly installed signs. Readability: Clear signage ensures that drivers can quickly obtain information and respond promptly. Poor readability increases the likelihood of risky driving behaviors, such as sudden braking or abrupt lane changes. Continuity: Guide signs should announce upcoming destinations and maintain consistency before and after exits. For instance, exit preview signs should align with the information presented at the exit and at branching points. In distance signs, the first-listed destination should correspond with the upcoming exit's information, ensuring seamless guidance until drivers reach their destination. Consistency: Sign information must remain consistent across the highway network. For instance, the top-listed destination on distance signs should align with corresponding exit preview signs, and mainline exit signs must match their previews. Additionally, toll station signs should correspond with relevant entry and exit signs to ensure smooth, uninterrupted navigation for drivers.
# Analysis of Mobile Edge Computing for Vehicular Networks +
## 3 Comparison of Task Delegation Scheme with Experimental Results
### JSON Parsing  
Our first computational task was to parse the raw GPS trace data from a JSON format to CSV format. This task involved simple text parsing in which the elements of JSON notation were removed and the desired attributes were left in a CSV format. We extracted its attributes from the raw data including: time stamps, GPS coordinates, and observed activity type at the given time stamp. The task was implemented in Java programming language and made use of a series of regular expressions to extract the target attributes. Once the records were reduced to the desired attributes, we then further reduced the data by taking only records with an observed activity type of driving or \"inVehicle\". Figure 3 shows an example record in the raw JSON format, and Figure 4 provides an example of the final CSV output after the parsing process.  
The raw data was continually collected and contained records with various activity types such as \"still\", \"inVehicle\", \"onFoot\", \"onBicycle\", and so on. Since we only targeted the records that were collected while the user was driving or \"inVehicle\", our parsing process resulted in a significant reduction in the size of the data set. For example, one week of GPS trace data in the raw JSON format is approximately 27,841 lines of text and has a size of 657 KB. Parsed to CSV format without removing the undesired records, the data is approximately 2283 lines of text and 369 KB. Once we have extracted all activity types other than \"inVehicle\", the data was approximately 109 lines of text and 36 KB. This reduction in data size greatly reduced the complexity of any future processing. In our test cases, this parsed data was the direct input to our next task. This process also demonstrated value in an edge computing system in which we may decide to perform trivial tasks at the edge of the network such as data pre-processing or reduction. In this case, performing the parsing task at the network edge can greatly reduce latency and communications overhead. Compression of the data would serve to further reduce the amount of data to be transmitted. However, for our test cases the data sets were small and compression was not necessary.
## 2 Theory
### Analytic Continuation, Global Analytic Functions, and Riemann Surfaces
#### 2.2.1 Analytic Continuation and Global Analytic Functions  
Roughly speaking, the analytic continuation is the operation with which it is possible to enlarge the domain of an analytic function \(f(\cdot)\), \(\Omega\). To formalize this idea, we provide the following definition [32].  
**Definition 1**.: _An ordered pair \((\Omega,f(\cdot))\) where \(\Omega\) is a domain \((\Omega\subset\mathbb{C})\) and \(f(\cdot)\) is an analytic function is called an analytic function element. A pair \((\Omega_{N},f_{N}(\cdot))\) is the direct analytic continuation of a pair \((\Omega_{1},f_{1}(\cdot))\) if the following is the case._  
\[f_{1}(z)=f_{N}(z)\quad\forall z\in\Omega_{1}\cap\Omega_{N}\quad(\Omega_{1}\cap \Omega_{N}\
eq\varnothing) \tag{12}\]_Alternatively, they are indirect analytic continuations of each other if the following set of analytic function elements exist between elements \((\Omega_{1},f_{1}(\cdot))\) and \((\Omega_{N},f_{N}(\cdot))\):_  
\[\{(\Omega_{2},f_{2}(\cdot)),\dots,(\Omega_{N-1},f_{N-1}(\cdot))\} \tag{13}\]  
_where \((\Omega_{k},f_{k}(\cdot))\) is the direct analytic continuation of \((\Omega_{k-1},f_{k-1}(\cdot))\) for \(k=2,\dots,N\)[32]._  
Based on the above statements, a global analytic function \(\mathcal{G}\) is a non-empty set of pairs \((\Omega,f(\cdot))\) that are the direct or indirect analytic continuation of each other [32, 35].The union \(\mathbb{M}=\cup_{i}\Omega_{i}\) of all domains \(\Omega_{i}\) of all analytic function elements belonging to \(\mathcal{G}\) is the domain of existence of \(\mathcal{G}\)[32]. Each analytic function elements \((\Omega,f(\cdot))\in\mathcal{G}\) is called a _branch_ of \(\mathcal{G}\)[35]. The value \(w_{0}\) provided by a pair \((\Omega,f(\cdot))\in\mathcal{G}\) at \(z_{0}\in\Omega\), i.e., \(w_{0}=f(z_{0})\), is called the value of \(\mathcal{G}\) at \(z_{0}\)[32]. A global analytic function \(\mathcal{G}\) can have several branches over the same domain. This behavior follows directly from the indirect analytic continuation's working, which can provide different results using different sequences of analytic function elements [32, 35]. In that case, \(\mathcal{G}\) is a multivalued function from \(\mathbb{M}\) to \(\mathbb{C}\)[32, 35].  
**Remark 1**.: _The set \(\mathcal{L}\) of all analytic function elements \((\Omega,h(\cdot))\) for which the following equation_  
\[e^{h(z)}=z\quad\forall z\in\Omega \tag{14}\]  
_holds is a global analytic function, \(\mathcal{L}\), and is named the global analytic logarithm [32]. Its domain of existence is \(\mathbb{M}=\mathbb{C}\). Any branch of \(\mathcal{L}\) has the form \(log(\cdot)=ln|\cdot|+i[arg_{\pi}(\cdot)]\), where \(ln|\cdot|\) and \(arg_{\pi}(\cdot)\) are the natural logarithm of the absolute value function and the principal argument function, respectively. Two distinct branches of \(\mathcal{L}\) differ by \(2p\pi i\) where \(p\in\mathbb{Z}\)[32, 35]._
## 4 Experiments
### Discussion  
The experimental results classified the structural elements and the indoor clutter of the six datasets with an average accuracy of 0.94, an average precision of 0.97, an average recall of 0.90, and an average F1 score of 0.93. In addition, the proposed method yielded improved performances for all evaluation metrics in comparison to the Auto-Classify Indoor function of the commercial software, as shown in Figure 12. In particular, all the metrics from the parking lot dataset and the apartment 1 dataset were 0.96 or higher. The improved performance of the apartment 1 dataset was attributed to the set's relatively low indoor complexity. Meanwhile, improved performance was observed in the case of the parking lot's dataset (among all datasets), despite its high indoor complexity.  
The proposed method yielded higher performance than the feature-based Auto-Classify Indoor function; this can be explained using Figure 13. Figure 13a shows the point cloud that is classified into the indoor Auto-Classify function and the structural elements, and Figure 13b is the actual target object. The highlighted area of Figure 13b is where the plasterboard was loaded on the floor. Therefore, it is appropriate to remove the plasterboard as indoor clutter objects. However, the point cloud of the plasterboard in the Auto-Classify Indoor function has vertical geometrical features, such as the wall, and it has therefore been classified as structural elements. Thus, the proposed method can operate powerfully even for indoor clutter objects with geometrical features similar to those of the structural elements.  
The advantages of the proposed method were summarized above; however, it did not perform outstandingly in all experimental datasets. In particular, the results from datasets from lecture rooms 1 and 2 exhibited high performances in the case of the Auto-Classify Indoor function, but by a narrow margin. This was because the proposed method performed weakly near the window and the door, as shown in Figure 14. The proposed method was developed based on a 2D projection approach. The point cloud obtained from the window is unstable, and the points of the structural elements that exist above the door can be classified as indoor clutter in the DBSCAN of the proposed method. The authors reviewed the experimental results of the proposed method and found that most errors occurred at the upper part of the window and the door. Therefore, it is expected that an  
Figure 12: Comparison between the proposed method and the commercial software Auto-Classify Indoor function.  
improvement in these limitations in the future could remove the indoor clutter objects more accurately.
## 5 Conclusions and Future Directions
DK is not currently included in cement standards (e.g., EN 197-1) likely due to its variable composition and limited performance data. Differences in DK particle fineness, amorphous content, and acidity across production sites present a major challenge for standardisation. However, these barriers could be overcome through better classification, much like the acceptance pathway for other SCMs or AAC precursors. To support the broader adoption of DK in binder systems for mortars and concrete and translate laboratory demonstrations to industrial applications, future work should prioritise the development of standardised mix design protocols that can account for DK variability. More studies focusing on further mix design optimisation both in PC systems and AAC systems are required for further strength enhancement particularly towards structural concrete applications. This includes optimising dosage levels in both PC and AAC systems to ensure consistent strength and workability, particularly with respect to activator concentration, precursor combinations, and fresh-state properties. Importantly, the durability and long-term performance including reinforcement compatibility, chemical resistance, and dimensional stability of DK-cement systems in concrete applications must be thoroughly investigated under both accelerated and real-world conditions. For AAC systems, in particular, further investigation into reaction kinetics, pore refinement, and gel evolution (e.g., C-(A)-S-H, N-A-S-H) will be essential to clarify the link between mix chemistry and performance. These efforts should be guided by microstructural characterisation and benchmarked against relevant construction standards to support eventual qualification and code compliance. Overall, the review indicated that DK offers a promising route for sustainable binder development, especially in combination with other SCM or precursors. Based on the quantities of concrete produced worldwide, the use of DK in concrete could outweigh the need for DK disposal, providing a useful outlet for this voluminous hazardous material, alternative to landfilling. However, realising this potential will require not just broader experimentation, but a shift in how such industrial by-products are classified, tested, and incorporated into construction norms. Robust life-cycle assessments, economic feasibility studies, and standardised performance validations are necessary to translate this potential into widespread commercial use. Conceptualization, M.E. and M.M. ; methodology, M.E. ; investigation, M.E. ; writing--original draft preparation, M.E. ; writing--review and editing, M.M., A.M. and M.A.C. ; visualisation, M.E. and M.M. ; supervision, M.M., A.M., R.S., M.A.C. and O.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. No new data were created or analyzed in this study. The authors declare no conflicts of interest.
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.5 A-Leach  
LEACH protocol was modified by [PERSON] [76] to achieve better security with less energy consumption and minimal data overhead. The protocol combines the features of Security-LEACH [77] and the Time-controlled Clustering Algorithm (TCCA) [78] that  
size would decrease with distance from BS. The CH is selected considering residual energy, weight factor, and distance as major parameters. Simulation results proved improved network lifetime but do not discuss other factors such as throughput, average residual energy, and network stability.
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 4 DiffServ-Based Dynamic Cooperative MAC Protocol
### MAC Sub-Protocol for on-Demand Communication
#### 4.2.2 Data Transmission Phase  
The pair \((s_{i},r_{i})\), having completed the negotiation phase, initiates the data transmission phase, and transmits packets by a data channel according to ACBS. In normal scenarios, sender \(s_{i}\) sends a DATA packet to receiver \(r_{i}\), and then receiver \(r_{i}\) replies with an ACK packet to sender \(s_{i}\) after a time duration of SIFS when the OC procedure is completed. Note that both the DATA and ACK are transmitted in DT-OR mode, and both the sender and the receiver set their parity fields as \"\(11\)\", in order to avoid the disturbance from other pairs in the same data channel. The schematic diagram of data transmission phase is presented in Figure 5.  
Note that some problematic scenarios might occur in this phase. _P.Case 1_: several pairs with different priorities select the same data channel, whereas pair \((s_{i},r_{i})\) has the highest priority (SP and PP). _P.Case 2_: several pairs of different priorities select the same data channel, whereas pair \((s_{i},r_{i})\) does not have the highest priority. _P.Case 3_: several pairs of the same priority select the same data channel. _P.Case 4_: the number of pairs, which intend to communicate, exceeds the number of available data channels.  
The above scenarios will first be solved by the first-come-stay policy, i.e., the pair that switches to the data channel earliest will occupy the data channel; meanwhile, other co-existing pairs should switch to other data channels according to their ACBS. However, if several pairs with different priorities select the same data channel, only the pair with the highest priority can stay. Moreover, if several pairs with the same priority select the same data channel, all the collision pairs switch to the next data channel according to their ACBS.