esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 1 Introduction  
The efforts made in quantum technologies have increased rapidly in recent years, and the number of filed patents and publications have increased together with the number of proposed projects. However, the impact of such technologies on the market remains limited. The reasons for this are several barriers preventing industrial transfer, both technological and related to perception from the industry. Here, we draw a link between existing technologies, protocols, and devices with potential industrial markets, as an attempt to identify where and how quantum technologies may impact relevant market segments. Covered aspects, ranging from enabling technology to market considerations, are illustrated in Figure 1.  
The paper is organized as follows. Section 2 introduces quantum key distribution (QKD) systems and networks, based on both discrete and continuous variables, discussing main implementation issues and proposed solutions. Section 3 provides a technological overview of the devices and the  
Figure 1: Review structure. Note: QKD = quantum key distribution.  
sub-systems needed in a QKD system (light sources, detectors, quantum repeaters, and random number generators). Application scenarios are presented in Section 4, along with relevant types of QKD systems and suitable technologies. Section 5, covering market perspectives, discusses the cost factors that may limit or delay the introduction of commercial QKD systems. Section 6 outlines an industrial roadmap based on the considerations above, covering the application scenarios and markets and ongoing public funding efforts. Section 7 gives an overview of the standardization of QKD systems. Finally, Section 8 draws conclusions.
# Development of a Multi-Objective Optimal Design Approach for Combined Water Systems
## 3 Application and Results
### Comparison with Existing Systems  
To judge the suitability of the optimal designs for the stormwater drainage system and the water distribution system, the optimal designs were first compared with the existing designs. The Pareto-optimal solutions for the existing design and the optimal design of the stormwater drainage network were derived, as shown in Figures 8 and 9 and Tables 1 and 2.  
Figure 8: Pareto-optimal solutions for the optimal design of water distribution systems.  
Figure 7: Layout of water reclamation and reuse systems.  
The comparison between the optimal and the existing designs was performed based on the closest designs. The difference in resiliency in Figure 8 and Table 1, which show a comparison of the water distribution system, was 0.006, which means that the difference between the existing and the optimal designs was not drastic, but the design with 1.2% (0.006) better resiliency led to better result values in terms of construction cost.  
The existing stormwater drainage network was designed based on the 10-year frequency design precipitation intensity, and the optimization was performed using 30-year frequency flood volume minimization as the objective function. The difference between the existing and the optimal designs was drastic. In particular, it was found that there was a \(28.056\times 10^{6}\) L difference in flood volume at a 30-year frequency design precipitation intensity.
## 4 Performance Analysis
### Engineering Applications
A higher matching degreebetween \(T\) and \(F\) corresponds to a larger value of \(NCC(x,y)\). The matching degree of the template image at various positions within the target image can be determined by comparing the similarity between two images and identifying their correlation. Subsequently, the TCDE algorithm can be employed to search for multiple peaks, thereby identifying multiple optimal solutions. Figure 14 depicts the target PCB image with dimensions of 393 pixels \(\times\) 422 pixels, while Figure 15 illustrates a template resistor image that is 34 pixels \(\times\) 51 pixels in size. Within the target PCB image, there are four regions that resemble the pattern in the template resistor image, indicating the presence of four peaks. The matching process transforms into a multi-peak optimization function after the application of NCC, as depicted in Figure 16. From the graph, it can be observed that this is a standard multi-peak optimization problem, with four peaks in the search space, representing the four optimal matching positions. Additionally, there are many irregular local peaks in the search space, which are significant sources of interference. The objective is to avoid all local peaks while accurately identifying all global ones. However, due to the abundance of local peaks, the algorithm can easily become trapped, making it difficult to locate the global peaks, which are narrow and hard to detect. As a result, the problem poses significant challenges. Table 6 below summarizes the performance of the improved TCDE algorithm for different population sizes (30, 50, 100, 150) and compares it with the experimental results of various multi-peak optimization algorithms. Unlike the previously used evaluation metrics, which were suitable for continuous solution spaces of multi-peak optimization functions, the current image multi-target recognition involves a finite search space. Specifically, the  
Figure 16: Three-dimensional PCB target image and template image matching diagram. Figure 14: Target PCB image. Figure 15: Template image. pixel size of target image 3 is 393 \(\times\) 422 = 165,846. Therefore, the original metrics are no longer applicable to the current algorithm. The number of detected pixels, which represents the consumed FEs during the optimization process, was used as a performance metric in this experiment to demonstrate the effectiveness of the algorithm. The use of fewer pixels used when finding all peaks indicates better algorithm performance. Table 6 displays the comparison results of the TCDE algorithm with 15 advanced multi-peak optimization algorithms for different population sizes for the PCB multi-template recognition problem, with the similarity evaluation function being NCC. The experimental results indicate that the TCDE algorithm consistently uses the fewest pixels. This demonstrates the superiority of the TCDE algorithm and its effectiveness in solving image multi-target recognition problems.
# A Low-Order Series Approximation of Thin-Bed PP-Wave Reflections
###### Abstract  
The study of thin-bed seismic phenomena is important in crustal, exploration and engineering seismology. Presently, seismic reflectivity theories based on single-interface assumption are widely used though they are only suitable for thick deposits. Thin-bed reflectivity theories are established on complex propagator matrices and are difficult to be applied to reveal thin-bed properties directly. Therefore, an approximation of thin-bed PP-wave reflection coefficients (\(R_{PP}\)) is derived in this paper. First, the relationship between thin-bed \(R_{PP}\) and incidence angles is analyzed through series expansion method. For PP-wave, its reflection coefficients are even power series functions of sine incidence angles. Then, for small incidence, \(R_{PP}\) of the thin bed is further simplified into a second-order series approximation with respect to the sine incidence angles. Simulations and accuracy analyses of the approximate formula show that approximation errors are smaller than 5% as the incidence angles smaller than 20 degrees. Based on this approximate formula, an approach is given for estimating thin-bed properties including P-wave impedance ratios and thickness. The estimation approach is applied in properties estimation of a thin bed model. Perfect performances of the model example show the future potentiality of the approximate formula in thin-bed Amplitude-Versus-Offset (AVO) analysis and inversion.  
Keywords:thin bed; PP-wave reflection coefficients; approximate formula; series expansion method +
Footnote †: journal: Applied Physics Letters
# Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices
## 2 Research Background and Related Works
### k-Nearest Neighbors (kNN) Search Problem
To evaluate the performance of \(k\)NN search algorithms, accuracy and search time are generally considered as the main metrics [10]. In practice, the system may have multiple candidates; we define the function \(CheckANN(q,\vec{x_{j}})\) to verify whether the item \(x_{j}\) is one of the \(k\)NN results of \(q\) or not. The index \(\vec{x_{j}}\) is used to eliminate memory copying during the search process, in order to increase search performance. Because the \(k\)NN results are temporary sorted over time, the qualities of \(k\)NN candidates are not only determined by the thresh \(P_{1}\) but also the number of checked items.
# RJ-RRT: Improved RRT for Path Planning in Narrow Passages
## 5 Conclusions  
This paper proposes a new planning algorithm RJ-RRT based on the RRT algorithm to solve the planning problem in narrow channels. A new greedy sampling space reduction and environment judgment strategy are proposed in RJ-RRT. While accelerating the expansion of random tree toward the goal area and reducing redundant nodes, it can identify narrow passages and rely on the subtrees expend inside to explore passages.  
Compared with RRT and RRV algorithms, RJ-RRT has better adaptability to different environments and maintains a faster planning speed. In addition, another advantage of RJ-RRT is that easy to combine with other RRT variant algorithms. However, the RJ-RRT still has limitations. For example, is easily affected by the expand size. Therefore, in future research, consider combining adaptive expand size and selectively merging multiple Gap Spaces, while RJ-RRT should also be extended to 3D environments and incorporate robot kinematics, making our algorithm closer to practical applications.  
Conceptualization, Q.C. and Y.W.; methodology, Q.C.; software, Q.C.; validation, Q.C. and Y.W.; formal analysis, Q.C.; investigation, Q.C.; resources, Q.C.; data curation, Q.C.; writing--original draft preparation, Q.C.; writing--review and editing, Q.C.; visualization, Q.C.; supervision, Y.W.; project administration, Q.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Chongqing Science and Technology Bureau (cstc2017 zdcy-zdyfX0042).  
Not applicable.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
RRT Rapidly exploring Random Tree  
PRM Probabilistic Roadmap Method  
RRV Rapidly exploring Random Vines  
RJ-RRT Reduce-Judge RRT  
Figure 11: Approximation of three algorithms to the shortest path.
# Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events
## 3 EMR Public Sentiment Analysis System Design
### Data Collection Based on Topic Similarity
#### 3.1.2 Evaluation Experiment
To reduce subjectivity and ensure consistency, a double-checking process was employed whereby each data entry was reviewed by two independent annotators. This could lead to issuing public sentiment warnings for irrelevant posts. The results showed that after the addition of the filtering system, the accuracy of the content of the top ten posts with positive and negative sentiment values improved, enhancing the efficiency of monitoring emotions and providing better public sentiment warnings. Further analysis of the experimental data revealed that without information filtering, the post ranked fourth in negative sentiment was published by \"Chong'er Girl\", mainly introducing a whole-house heater but only mentioning \"electromagnetic radiation\" once in the text, which was unrelated to the keyword theme. However, after adding the topic similarity filter, the post ranked fourth was published by \"Qishan Dao Ren Lin Chengbing\", which extensively introduced the hazards of electromagnetic radiation, consistent with the keyword. The experimental results indicate that by adding keyword filtering to exclude posts with low topic similarity, the precision of emotional monitoring can be improved. This reduces the occurrence of situations where posts only contain the keyword but are unrelated in actual content, avoiding unnecessary public sentiment warnings for irrelevant posts, and thus improving work efficiency.
# Limitations of Large Language Models in Propaganda Detection Task
## 5 Results
### LLM on SemEval2020--English Data, [PERSON] et al. Approach
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Technique\textbackslash{}Model** & & & & & & & & & \\ \hline
**Appeal to authority** & 0.19048 & 0.24000 & 0.32432 & 0.22857 & 0.11765 & **0.31579** & 0.00000 & 0.00000 & 0.40000 & 0.24000 \\ \hline
**Appeal to fear/prejudice** & 0.00000 & 0.00000 & 0.48276 & 0.00000 & **0.54545** & 0.00000 & 0.00000 & 0.00000 & 0.53333 & 0.00000 \\ \hline
**Bandwagon, reductio** & & & & & & & & & \\
**ad hitterum** & 0.00000 & **0.12500** & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 \\ \hline
**Black-and-white fallacy** & 0.00000 & 0.00000 & 0.18182 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & **0.28571** & 0.00000 \\ \hline
**Causal oversimplification** & **0.50000** & 0.37500 & 0.29630 & 0.31579 & 0.11111 & 0.12500 & 0.00000 & 0.00000 & 0.41509 & 0.37500 \\ \hline
**Doublet** & 0.54545 & 0.53465 & 0.24390 & 0.56757 & 0.00000 & 0.41509 & 0.06667 & 0.06897 & 0.55172 & **0.57447** \\ \hline
**Exaggeration, minimization** & 0.64000 & **0.64706** & 0.35088 & 0.057142 & 0.05128 & 0.00000 & 0.00000 & 0.00000 & 0.56250 & 0.60274 \\ \hline
**Flag-waving** & 0.00000 & 0.00000 & 0.15385 & 0.00000 & 0.10811 & 0.00000 & 0.00000 & 0.00000 & **0.23256** & 0.00000 \\ \hline
**Loaded language** & **0.93617** & 0.93617 & 0.90625 & 0.89600 & 0.77876 & 0.71698 & 0.30380 & 0.34568 & 0.84034 & 0.93233 \\ \hline
**Name calling, labeling** & 0.74286 & 0.72165 & **0.79630** & 0.73684 & 0.76636 & 0.76364 & 0.07407 & 0.00000 & 0.57895 & 0.66667 \\ \hline
**Repetition** & 0.65789 & 0.67327 & 0.60274 & **0.68235** & 0.59459 & 0.53846 & 0.10000 & 0.15000 & 0.37500 & 0.47273 \\ \hline
**Slogans** & 0.10000 & **0.23529** & 0.10000 & 0.09523 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 \\ \hline
**Thought-terminating cliches** & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 \\ \hline
**Whataboutism, straw men, red herring** & 0.33333 & **0.45283** & 0.43243 & 0.27273 & 0.08333 & 0.09091 & 0.00000 & 0.00000 & 0.16667 & 0.33333 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: F1 scores for each of the propaganda techniques. same seemed to be true for _name calling, labeling_ and _repetition._ None of the models could correctly predict any instance of the _thought-terminating cliches_ category. In conclusion, we do not think that GPT models are a sufficient tool for processing propaganda techniques detection because the results we obtained seemed to be random and no reliable conclusions could be drawn from them.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 5 Experimental Results  
### RQ1: How Effective Is CIB? How Much Improvement Can It Obtain Compared with the Baselines? Tables 4, 5, 6 and 7 respectively show the comparison results of \(F_{1}\), AGF, MCC, and AUC for our CIB and the baselines on each of 11 benchmark datasets. For each table, the best value on each data is highlighted in bold. The row 'Average' presents the average performance of each model across all benchmark datasets. The row 'Win/Tie/Lose' shows the comparison results of Wilcoxon signed-rank test (5%) when comparing our CIB with each baseline. Specifically, Win/Tie/Lose represents the number of datasets our CIB beats, ties with, or losses to the corresponding baseline, respectively. We use different background colors to show the results of effect size test (i.e., Cliff's \(\delta\)). Specifically, the deep gray, the light gray, and the silvery gray  indicate that the proposed CIB significantly outperforms the corresponding baseline with large, moderate, and small effect size, respectively. If the baseline significantly outperforms our CIB or the effect size is negligible, then the corresponding table-cell of this baseline is marked with a white background. Checkmark \(\checkmark\) represents that the corresponding method has no significant difference when compared with the best method. Figure 1(a)-d show the corresponding boxplots of \(F_{1}\), AGF, MCC, and AUC for all methods on \(11\) NASA datasets, where the horizontal line in the box denotes median and the asterisk indicates mean. Tables 8-11 separately present the 95% confidence interval of \(F_{1}\), AGF, MCC, and AUC for all methods on each of the 11 NASA datasets. Figure 2: _Cont._  
With respect to \(F_{1}\) (see Table 4), we notice that, on average, CIB obtains the best \(F_{1}\) as 0.381 across all datasets, which improves the performance over the baselines by at least 9% (see AdaC2 [45]). The \(F_{1}\) of CIB ranges from 0.148 (on dataset PC2) to 0.621 (on dataset PC4) across all 11 datasets. According to the results of 'Win/Tie/Lose' and the background of table cells, we notice that our CIB nearly always significantly outperforms AdaBoost and None with large effect size on all 11 datasets. Moreover, on most datasets, CIB significantly outperforms MAHAKIL, AdaC2, AdaBoost, and SMOTE with at least medium effect size and significantly perform worse than the baselines on at most one dataset.
# Dynamic Demand-Responsive Feeder Bus Network Design for a Short Headway Trunk Line
## 2 Literature Review
### The Dial-a-Ride Problem  
Dial-a-ride problems (DARPs) have been considered as a variant of DRT problems; however, in a DARP problem, the focus is on pickup and delivering passengers from the exact points to defined points in an allowed time window [33; 34]. The pickup and delivery problem with time windows (PDPTW) and dial-a-ride problems with time windows (DARTWs), which are both a generalization of the vehicle routing problem (VRP), are similar to DRT and DARP problems. All these problems involve providing point-to-point transportation services while considering spatial-temporal information about passengers.  
The main goal of these problems is creating the best routes for vehicles where both vehicles' and passengers' traveling costs are minimized. PDPTWs and DARTWs are essentially linear models; however, in recent years, by considering new variable and more realistic circumstance factors these models turned out to be dynamic and nonlinear [35]. [PERSON], [PERSON] [36] proposed a metaheuristic method to solve a single-depot DRT problem by minimizing the operator's cost. They considered a static DRT problem which considered fixed demands and routes. However, this model could only be implemented in small networks. [PERSON], [PERSON] [37] solved a DRT algorithm that was modeled as a rich traveling salesman problem (RTSP). Although the proposed model aimed to minimize the sum of the costs from all routes and a Golden Ball metaheuristics was implemented as a solution, the model did not coordinate with another transit system. [PERSON], [PERSON] [38] developed an online dynamic insertion algorithm with demand forecasts, aiming to minimize passengers' travel costs. The results of their model showed that the proposed model could reduce passenger travel and waiting times at the same time. The issue they needed to address in future studies was the possibility of relocating shared vehicles when there is a high volume at a certain station. Recently, [PERSON], [PERSON] [39] formulated an exact solution framework to address the capacitated multi-trip vehicle routing problem with time windows. In this approach, they established two computationally efficient lower bounds. These bounds were utilized within the framework to produce a condensed set of columns encompassing any optimal multi-trip vehicle routing solution. Subsequently, a branch-and-cut method was employed to identify the optimal solution.
# Open-Source MQTT-Based End-to-End IoT System for Smart City Scenarios
## 4 Proof-of-Concept
### Raspberry PI 3B+ Node
#### 4.3.1 Mosquito  
The logical role assigned to the Raspberry Pi node is the MQTT broker, which allows communication between different MQTT clients. Mosquito is an open-source lightweight message broker that implements the MQTT protocol versions 5.0, 3.1.1 and 3.1 and is suitable for all devices from low-power single-board computers to full servers [22]. The rationale behind MQTT is its light weight; in fact, it is intended for devices with limited power capabilities, or when we have to deal with constrained and unreliable networks,  
Figure 4: MQTT Packets Exchange.  
e.g., cellular networks, affected by a high packet loss rate [34]. These advantages make it a feasible solution for web-based data monitoring operations [35].  
The MQTT clients are implemented on the Android smartphones running the developed app, acting as MQTT publishers by publishing the readings of the wearable device's sensors, as well as on a webservice dashboard, later illustrated in Section 4.4, acting as a MQTT subscriber.  
The main role of the broker is to provide a way for routing the packets on a certain topic to the clients subscribed to that specific topic. However, the broker has several other responsibilities; one of those is authentication: in order to secure the connection of the IoT system, it is important not only to define the authentication credentials, such as a username or password, to be used by every client to sign into the broker but also establish an encrypted communication between the broker and the clients through TLS and SSL. In our case, we have created a custom configuration for Mosquitto, which let us define a password and username that will be used to log into the broker, but, more importantly, since we are dealing with a webservice client, we had to enable MQTT over websockets by defining the socket number the broker has to listen to as 9001.
## 3 Proposed Scheme to Secure Device Layer Network
### Architecture of M2M Layer  
In the proposed architecture for the M2M device layer (Figure 2), device networks are linked to network layer via gateways depending on particular technologies (Wi-Fi, Zigbee, 4G and 5G cellular networks, etc.).  
The base stations (Figure 3) incorporating gateways are supposed with high (or unlimited) computational and energy resources.  
Figure 1: ETSI M2M system architecture—Functional view.  
Figure 2: Architecture of M2M Layer.  
They perform the following tasks:  
* Registering a new node on the network as a new entity.
* Identity and authentication management of secure communications.  
M2M nodes (sensors, smart objects, etc.) collect and transmit data packets before forwarding them to the M2M gateways. After receiving the packets, an M2M gateway manages the packets and provides appropriate paths for forwarding these packets to an M2M application server or to other device nodes on different device networks via wired/wireless networks. Of course, gateways perform appropriate protocol conversion and data segmentation or assembly (for example, from IP to 802.15.4 and vice versa).