esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Experiments
### Evaluation Metrics  
The confusion matrix is an essential tool for assessing the performance of machine learning models. It presents a structured table that juxtaposes the actual and predicted class labels, as detailed in reference [100]. This matrix facilitates the calculation of a range of performance metrics.  
* True Positive (TP): These are the instances that the model correctly predicted to be positive. For example, if a spam filter correctly identified an email as spam, this is a true positive.
* False Negative (FN): These are the instances that the model incorrectly predicted to be negative. In the spam filter example, if it mistakenly classified a spam email as legitimate, this is a false negative.
* True Negative (TN): These are the instances that the model correctly predicted to be negative. Returning to our spam filter, if it accurately identified a non-spam email as non-spam, this is a true negative.  
* False Positive (FP): These are the instances that the model incorrectly predicted to be positive. In the spam filter context, if it mistakenly classified a legitimate email as spam, this is a false positive. Equation (32) [101] illustrates the most basic and fundamental metric, accuracy, which can be derived from the confusion matrix. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (32)  
It is common to evaluate the model using a variety of additional metrics, including recall, precision, and the F-score. Precision is determined by dividing the number of true positive results by the total number of predicted positive results, encompassing both correct and incorrect identifications. This metric, also known as positive predictive value, is calculated using Equation (33) [101]. Recall, defined in Equation (34) [101], assesses the proportion of actual positive instances that the model correctly identifies among all instances that should have been recognized as positive. The F-score, computed using Equation (35) [102], serves as the harmonic mean of precision and recall, providing a balanced measure of the model's performance.  
\[Precision=\frac{TP}{TP+FP} \tag{33}\]  
\[Recall=\frac{TP}{TP+FN} \tag{34}\]  
\[Fscore=\frac{2*precision*recall}{precision+recall} \tag{35}\]  
In this scenario, the goal is to enhance metrics including the F-score, accuracy, recall, and precision, as outlined by the evaluation criteria.
## 3 Results and Discussion
### Technological Reliability of the Studied Systems
The presented results indicate that the reliability of COD removal in household WWTPs with activated sludge is lower than that in CWs. **Total nitrogen. ** Both analyzed activated sludge WWTPs were characterized by extremely low reliability of TN removal. The probability that the concentration of TN in treated wastewater will not exceed the standard value (30 mg/dm\({}^{3}\)) was 0% for facility no. 1 and 5% for facility no. 2 (Figure 6). In the case of TN removal, facility no. 1 operated deficiency throughout the whole year and provided no assurance of passing inspection procedures. On the other hand, in WWTP no. 2, above-normative values of TN in treated wastewater occurred for a period of 338 days per year. [PERSON] [33], while analyzing a solution identical in terms of construction, but without a classical preliminary settling tank, obtained a reliability of TN removal at the level of 24%. The reliability of TN removal in a small SBBR was only 12.2% [35]. In contrast, TN removal reliability in two other household hybrid CW WWTPs operating in the RNP was much higher and amounted to 35% and 89% [36]. On the other hand, [PERSON] et al. [33] obtained 94% reliability of TN removal in a household hybrid CW. The analysis of the results of the conducted study through the prism of the results reported in the literature shows that the reliability of TN removal is lower in household WWTPs with activated sludge than in CWs. **Total phosphorus. ** The reliability of TP removal in the tested household WWTPs with activated sludge was even lower. The probability that the content of TP in the effluent from facility no. 1 will meet the permissible value at the operator risk of \(\alpha\) = 0.05 was 1%. For facility no. 2, this value was determined to be 0% (Figure 7). During the research concerning the effluent from the analyzed facilities, no cases were observed when the values were below the permissible level 5 mg/dm\({}^{3}\)[47]. The obtained indicators allow concluding that household WWTPs with activated sludge are faulty in terms of TP removal throughout the year. The obtained results do not give a chance for a positive assessment of the facilities regarding the content of TP in treated sewage. In a similar facility, deprived of a classical mechanical treatment stage, a low technological reliability at the level of 5% was found [33]. On the other hand, the reliability of TN removal in a small SBBR was only 21.7% [35]. In contrast, a study conducted by [PERSON] et al. [36] showed that TP removal reliability in two other household hybrid CW WWTPs operating in the RNP was much higher and amounted to 87% and 100%. Moreover, [PERSON] et al. [33] in a hybrid household CW achieved 100% reliability of TP removal. Similarly to other indicators, the reliability of TP removal in household WWTPs with activated sludge was lower than in CWs.
## 3 Methodology
### Prediction Using Machine Learning  
Using the re-trained Inception-ResNet-v2 model to determine the surface strength of rocks is virtually an image classification process. In training the DNN, the inputs are the spectrograms, and the labels are the values of surface strengths. After training, for a new spectrogram, the DNN can give the probabilities that the spectrogram belongs to each of the strengths, and classify it into a class according to the maximum probability, as shown in Table 1.  
It can be seen that the prediction results are discrete. However, surface strength is a continuous variable. To resolve this problem, the probabilities are regarded as the spectrograms' features extracted by the DNN model. Therefore, every spectrogram has 10 features and one label (strength value). Then, the relationship between the features and the strength values can be fitted with regression algorithms. The regression algorithms used in this research included the K-Nearest Neighbor [34], the Support Vector Machine [35; 36], and the Random Forest [37].
# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 4 Experimental Results and Analysis  
To validate the effective application and performance improvement of the proposed RSVQ-Diffusion model in the field of remote sensing image generation, both the original model and the RSVQ-Diffusion model were trained on publicly available remote sensing image datasets. Additionally, ablation experiments were designed to verify the improvement effects, and comparisons were made with existing text-to-remote-sensing image generation models.
## 3 Coupled Monorail Train-Bridge System
### Bridge Subsystem  
Figure 2 shows the finite element model of the bridge subsystem. The arch ribs are divided into 23 segments along the axis of the bridge. The transverse bracing of the arch rib and arch rib are modeled by three-dimensional beam elements. The suspenders and tied bars are modeled using three-dimensional truss elements. The effect of pre-tension on a bar member is simulated by specifying an initial tensile strain in the member. The steel truss girder, Y-shaped rigid frame, and piers are modeled using 3D beam elements. Each of the beam elements has two nodes, and each node has six DOFs. In total, the bridge model has 3257 nodes and 6515 elements, which include 136 truss elements and 6319 beam elements. In light of the boundary conditions, the steel truss girder is supported using eight basin type rubber bearings placed on the bridge piers, which are fixed at the bottom. A linking bar support is arranged at the pier top of the transition pier. In order to control the displacement of steel truss girder under the action of the braking force, wind force, and earthquake force, the bottom chord bar of the steel truss girder is connected with the beam of the abutment pier. The mechanical properties of the materials are listed in Table 1.  
Figure 1: The layout of the Caiyuanba Yangtze Bridge (unit: cm): (**a**) elevation view; (**b**) overview.  
\[[C_{b}]=\alpha[M_{b}]+\beta[K_{b}], \tag{6}\]  
where \([C_{b}]\), \([M_{b}]\) and \([K_{b}]\) are the damping, mass, and stiffness matrices of the bridge, respectively; \(\alpha\) and \(\beta\) are the Rayleigh damping coefficients, which can be calculated by the following formula:  
\[\alpha=\frac{2\omega_{i}\omega_{j}(\xi_{i}\omega_{j}-\xi_{j}\omega_{i})}{\omega _{j}^{2}-\omega_{i}^{2}},\beta=\frac{2(\xi_{j}\omega_{j}-\xi_{i}\omega_{i})}{ \omega_{j}^{2}-\omega_{i}^{2}}, \tag{7}\]  
where \(\omega_{i}\) and \(\omega_{j}\) are the \(i_{\rm th}\) and \(j_{\rm th}\) natural vibration frequencies; \(\xi_{i}\) and \(\xi_{j}\) are the corresponding damping ratio. According to [PERSON]'s research [31], for the vehicle-bridge coupled system, the fundamental frequency of the structure can be used as the reference frequency \(\omega_{i}\) and the maximum excitation frequency (\(\omega_{j}\)) produced by track irregularities can be used as the reference frequency \(\omega_{j}\). In this study, the \(\omega_{10}\) is close to the \(\omega_{j}\). Therefore, the first and tenth order frequencies were used to calculate the Rayleigh damping coefficient and the damping ratio was selected as 0.5%. The calculated values of \(\alpha\) and \(\beta\) are 0.0113 and 0.0019, respectively.  
The displacement equations of the bridge subsystem can be expressed as follows:  
\[[M_{b}]\Big{\{}\ddot{X}_{b}\Big{\}}+[C_{b}]\Big{\{}\dot{X}_{b}\Big{\}}+[K_{b} ]\{X_{b}\}=\{P_{bv}\}, \tag{8}\]  
where \(\Big{\{}\ddot{X}_{b}\Big{\}}\), \(\Big{\{}\dot{X}_{b}\Big{\}}\) and \(\{X_{b}\}\) represent the nodal dynamic acceleration, velocity, and displacement vectors of the bridge; and \(\{P_{bv}\}\) is the external force vector due to the moving train.
## 5 Performance Analysis and Numerical Results
### Results of V2X Performance Comparison
#### 5.1.2 Data Transmission Reliability  
The simulation results regarding the data transmission reliability in the grid road scenario and the adjusted geometric road scenario are shown in Figure 8. The comparative simulation results include four data transmission modes with the lowest data transfer rate (the lowest requirement for SNR): BPSK 1/2, QPSK 1/2, QPSK 3/4, and 16-QAM 1/2.  
Based on Figure 8a, it can be observed that in this scenario, 802.11 bd achieves an approximately 18% transmission reliability gain over 802.11p for both the BPSK 1/2 and QPSK 1/2 data transmission modes. The transmission reliability gain decreases to 13% for QPSK 3/4 and further decreases to 5.5% for 16-QAM 1/2. For more enhanced data transmission modes, the transmission reliability gain in this scenario is extremely marginal. The reason for this simulation result is that the reference data for the improvement in the transmission reliability in this paper are the SNR gains of 2-4 dB. The transmission reliability improvement of different data transmission modes can only be reasonably obtained within this range during implementation. However, the SNR requirements for different data transmission modes exhibit almost exponential growth in the simulation (see Figure 3), rapidly reducing the positive impact generated by the SNR gain as the data transfer rate increases. Derived from Figure 8b, the beneficial effects of the SNR gain ware within the intricate scenario, primarily attributed to the heightened complexity of the traffic environment. However, the transmission reliability gain remains stable (around 6%) across different data transmission modes. It does not decrease significantly with the increase in data transfer rate, as observed in the straightforward scenario.
## 4 Case Study
### Actuators  
Smart plugs, the automatic window opening system and relays are the main actuators in the case study. The actuation of devices needs to be carefully evaluated, as switching off lighting systems is a security risk. Indeed, switching off sockets can cause data loss from computer equipment, damage connected equipment, or interrupt ongoing experiments. Smart plugs are installed in each room and left 24 h each day. The number of plugs is sufficient to monitor electrical loads and remotely control the individual locations to which they are connected. The relays, on the other hand, are placed in the electrical box to monitor the loads and control the lights (on/off), and in the box to control the servomotor responsible for opening and closing the windows. Each of the actuators is able to record the total power consumption in W, the system status and the time of use. Historical data allows the use of machine learning logic to develop usage models and make future predictions. Finally, the actuators are complemented by the role of maintenance personnel and users who, through human-in-the-loop logic, can carry out the actions recommended by the system where automatic actuators are not or cannot be installed.
# A Multi-Robot Formation Platform based on an Indoor Global Positioning System
## 3. Configure the Platform for the Leader-Follower Formation
### Experiment Setup for Leader-Follower Formation Control  
Each robot gets their pose in real-time via the indoor global-positioning system. Specifically, the actual position of the robot is obtained through the Marvelmind Indoor Navigation System and actual orientation is obtained through MPU-6050.  
To form, maintain, and switch a certain geometric formation based on the controller besides its own pose, the leader's actual pose must be transmitted to each follower robot. The communication among robots is established as Figure 5, and the implementing process in detail as follows:  
Figure 5: Wireless network architecture for leader–follower formation.  
Step 1. The leader robot and follower robots connect to the same Wi-Fi network.  
Step 2. TCP-Server1 and TCP-Server2 are created by the leader robot and the host computer, respectively.  
Step 3. Each follower robot as a TCP-Client connects to TCP-Server1 and TCP-Server2. The pose of the leader robot is transmitted to each follower by the Wi-Fi network from TCP-Server1 to each TCP-client. Meanwhile, the pose information, including the follower's pose and the leader's pose, is transmitted to host computer from each TCP-client to TCP-Server2.  
During the process of controller calculation, the follower robots work out their desired pose according to Equation (1) and then calculate the errors according to Equation (2). Next, through Equation (3), they get the control inputs \(v_{i}\) and \(\omega_{i}\) which are the follower robots' line velocity and angular velocity; lastly, they move into the desired formation. The controller calculating process of each follower robot is shown in Figure 6.
## 3 Methodology and Data
### Network Mixing Patterns
#### 3.1.2 Measuring Assortativity Coefficient
A higher value indicates a stronger ability of a city to control pairs of other nodes in the network; thus, betweenness centrality primarily describes how a district's topological position influences its centrality. Therefore, this study selects both degree centrality and betweenness centrality as nodes' structural features. (2) Socioeconomic assortativity coefficient: The socioeconomic development level of cities and their population attributes are used as the city attributes to calculate the assortativity of the urban agglomeration network. This paper calculates the average values of GDP and permanent resident population for the sample cities from the China Statistical Yearbook (2017 to 2019) to eliminate the effects of random fluctuations on the socioeconomic development level of the cities. To facilitate the comparison of assortativity features across different urban agglomerations, the average GDP and average permanent resident population were standardized according to the scale of each urban agglomeration and used as node attributes. Unlike structural assortativity, attribute assortativity embedsthe socioeconomic attributes of cities within the network structure, describing the similarity of the attributes of cities at both ends of a relationship. This allows for an analysis of the internal mechanisms driving the formation of intercity relationships from the perspective of socioeconomic development levels. GDP is a crucial indicator measuring the economic scale and wealth level of districts, reflecting a region's overall economic level. Permanent resident population not only indicates the demographic scale of districts but also represents their potential market capacity, closely correlating with the intensity of economic activities, market potential, and demand. Therefore, this study selects GDP and permanent resident population as nodes' socioeconomic attributes.
# Path Optimization Using Metaheuristic Techniques for a Surveillance Robot
## 4 Optimization Methodology
### Metaheuristic Optimization Techniques Applied  
The three optimization techniques that are applied and compared are the following.  
* Genetic Algorithms: GAs emulate biological evolution to find optimal solutions in complex problems. They use selection, crossover, and mutation to refine a population of potential solutions iteratively. In surveillance trajectory design, they help generate and evolve efficient paths considering coverage, time, and distance limitations. By evaluating fitness and evolving generations, they converge towards improved strategies. GAs are versatile tools for optimization challenges [33]. GA utilized the following characteristic parameters: a crossover fraction of \(\{0.8\}\), a population size of \(200\), a constraint tolerance of \(\{1\times 10^{-3}\}\), an elite count of \(10\), a function tolerance of \(\{1\times 10^{-4}\}\), a migration fraction of \(\{0.2\}\), and a migration interval of [20].
* Particle Swarm Optimization: PSO mimics swarm behavior, where particles adjust their positions and velocities to find optimal solutions in multi-dimensional spaces. It combines individual and collective knowledge to converge towards better solutions iteratively. PSO's dynamic balance between exploration and exploitation makes it effective for complex optimization problems. In surveillance trajectory refinement,  
Figure 7: Software architecture.  
PSO iteratively adjusts trajectory parameters to enhance convergence towards optimal solutions, optimizing coverage, time, and constraints [34].  
In this case, the following parameters were used: the minimum neighbor fraction {0.25}, the self-adjustment weight {1.49}, and the social adjustment weight {1.49}.  
* Pattern Search: Leveraging PS for optimizing Hussar robot's trajectory involves systematic environment exploration to identify features like obstacles and open areas. As the two algorithms explained previously, it initiates with an initial path, iteratively adjusting it using diverse patterns aligned with identified features. Patterns leading to improvements are retained, while ineffective ones are discarded. Continuously refining the trajectory via PS helps the Hussar robot adapt to environmental nuances, ensuring effective area coverage. This dynamic approach tailors the trajectory to specific conditions, enhancing surveillance performance [35].  
Finally, in the case of PS, the following parameters were used: a constraint tolerance of {1 \(\times\) 10\({}^{-6}\)}, an initial mesh size of {1}, an initial penalty of {10}, a mesh contractor factor of {0.5}, a mesh expansion factor of {2}, a mesh tolerance of {1 \(\times\) 10\({}^{-6}\)}, a penalty factor of {100}, and a step tolerance of {1 \(\times\) 10\({}^{-6}\)}.
# Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era
## 5 Opportunities in the 5G Era
### D2D in On-Site Information Delivering  
Device-to-Device (D2D) communication, which refers to a radio technology that enables direct communication between devices (i.e., users) without data traffic going through any infrastructure node such as an access point or base stations, is recognized as one of the important technical components of the evolving 5G architecture [92]. By establishing the connection with each other directly, this type of communication can work in the absence of network operators or any central entity such as a base station [93]. Therefore, it is very useful for emergency situations where the cellular infrastructure is completely or partially dysfunctional. To help the build of emergency communication under such emergencies, many D2D applications have been proposed. One of the most important application scenarios is to help rescue missions by maintaining proximate communication among rescuers, communication between rescuers and victims, etc. [94; 95; 96]. The D2D technology can provide increased data rates and better QoS in emergency communication by building direct connections among users. In addition, the D2D communication can be used in a multi-hop manner to relay signals by connecting to other devices that reach operational base stations [97; 98; 99]. This sort of application can enable the extension of the network coverage or alternatively boost the existing network's capacity in critical conditions.  
There is also a large likelihood of encountering partial or total network failure in building interiors due to fire damage or network congestion in urban tall building fire events. The lack of prompt on-site communication may cause the IC to make wrong commands and delay the escape of the building occupants. Furthermore, due to the increased reliance on wireless sensors or devices to facilitate various operations before or during the ER in the future, the collapse of the network in buildings may cause even more severe impacts to ER operations. Therefore, it is essential to maintain the robustness of the communication network in tall buildings. One possible approach is to use the network slicing technology to create additional slices for emergency communication. However, this approach is only helpful when the network infrastructure is intact. Fire incidents in tall buildings may damage the cellular infrastructure in buildings causing total network malfunction. The D2D communication successfully solves this issue by providing direct communications among users without going through any infrastructure nodes. Thus, it will surely play an important role in future building fire evacuation in tall buildings. Moreover, another application of D2D communication is to directly deliver the live information of the fire event and evacuation guidance to occupants' mobile devices. This allows the occupants to be aware of the fire event in a timely fashion and take prompt actions.