esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Study Area and Data Sources
### Available Datasets
#### 3.2.2 Fucheng-1/Shenqi  
Developed by Tianyi Research Institute, \"Fucheng-1\" and \"Shenqi\" are two operational SAR satellites launched on 7 June 2023 and 24 September 2024, respectively. They deliver stable imaging, interferometry, and orbital control performance. With a strip-map mode resolution better than 2 m, they are the leading global commercial interferometric SAR satellites. On 20 November 2024, they achieved repeat-track dual-star interferometry with a vertical baseline within 150 m, and their shortest revisiting period was 4 days, demonstrating enhanced imaging and coherence capabilities. Their high-frequency revisiting and high resolution are beneficial in monitoring applications in defense, urban management, and disaster response. As shown in Figure 3, their high-resolution SAR images cover the Jinchuan mining area. \"Shenqi\" is based on the technology \"Fucheng-1\", offering better SNR and ambiguity control. Table 2 lists their specific parameters.
## 3 SAgic-IoT Platform Description
### Convolutional Neural Network Model
#### 3.5.2 Model Creation  
A model based on a convolutional neural network architecture was developed for the plant disease detection process. The model takes color 200 \(\times\) 200 images normalized to (0,1) as input for training and validation. The neural network architecture consists of five convolutional layers using 256, 128, 64, 32, and 16 filters, respectively. The kernel size for the 2D convolution window was 3 \(\times\) 3. Rectified linear unit (ReLU) was used as the activation model for each convolutional layer. MaxPooling layers were applied to each convolutional layer. In addition, a global average pooling layer was applied to convert the data to a column vector. This layer is connected to a dense layer of 10 output nodes (which represent the categories used in the model) that uses softmax as the activation model.
# Design Strategies for Mobile Click-and-Load Waiting Scenarios
## 2 Related Work
### Design of Waiting Experience Metrics  
The waiting experience is a comprehensive manifestation of users' subjective perceptions and objective behaviors in response to loading or response delays during the use of products or services. It not only affects overall user satisfaction but also involves multiple dimensions, such as time perception and emotional responses [35]. Thus, how can the quality of the waiting experience be measured? This question has become one of the key issues in current user experience design research.  
In recent years, scholars have progressively established multidimensional evaluation systems for waiting experience, which mainly encompass the following three aspects:  
1. User Experience Satisfaction Metrics: these originated from the Customer Satisfaction (CSAT) theory proposed in 1965 and typically utilize a five-point Likert scale to assess users' immediate evaluations upon completing product functions (ranging from \"very satisfied\" to \"very dissatisfied\"). This metric is widely applied in the fields of internet services, e-commerce, and software products as a fundamental tool for measuring user satisfaction with design, interface, and functionality [36; 37; 38].
2. Time Perception Metrics: These include methods such as the time perception technique and subjective loading speed questionnaires. The former focuses on users' ability to perceive and reproduce time intervals, reflecting differences in attention and cognitive load; the latter captures users' subjective ratings of loading time, thereby revealing the relationship between design fillers and users' intuitive impressions, which can assist designers in optimizing loading strategies [39].
3. Emotion-Related Metrics: Researchers employ instruments such as the PANAS scale [40] and the DASS-21 scale [41] to assess users' emotional states during waiting periods. In recent years, the Self-Assessment Manikin (SAM), a graphical emotion self-report tool based on valence-arousal level dimensions [42], has been widely used in user emotion research. This tool allows users to indicate their emotional state through intuitive cartoon figures, thereby avoiding potential misinterpretations associated with textual descriptions.  
Therefore, in designing waiting feedback, this study adopts a comprehensive approach by considering satisfaction, time perception, and emotional response. By combining questionnaire surveys with standardized scales, it conducts an in-depth analysis of users' psychological states and behavioral reactions during waiting and constructs a waiting experience evaluation framework that accurately reflects users' real experiences. This framework aims to provide both theoretical support and practical guidance for product optimization.
## 3 Research Design and Methodology, and Model Integration
### AHP Analysis Research Process and Data Collection  
In this study, the AHP questionnaire was designed to reflect the complexities of 5G antenna manufacturing, incorporating insights not just from industry specialists but also from experts across various related domains, including 5G technology, artificial intelligence, blockchain, manufacturing, and supply chain management. This interdisciplinary approach aimed to capture a comprehensive view of the industry by embracing a wide breadth of knowledge and experience.  
The data collection period extended over the month of December 2023. To qualify for our expert panel, individuals were required to hold at least a bachelor's degree and possess a minimum of ten years of professional experience. Out of the 44 returned questionnaires, 35 met the criteria for inclusion in our analysis. The selected responses adhered to a Consistency Ratio threshold of 0.1, a standard set to ensure data reliability.  
The demographics of the respondents, as elaborated in Table 5, were predominantly male (71.4%), with the majority aged in their 40s (45.7%). The respondents in their 50s constituted 28.6%, while those in their 30s made up 25.7%. The experience levels were diverse, with 22.9% having over ten years in the field, 60.0% with more than fifteen years, and 17.1% possessing over twenty years of expertise. This demographic diversity provided a rich and varied foundation for analysis, offering multiple perspectives to dissect and understand the complex dynamics of 5G antenna manufacturing.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(n\) & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline _RI_ & 0 & 0 & 0.58 & 0.9 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 & 1.49 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Saaty random index (_RI_) values for AHP consistency assessment and _CR_ calculation [115].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Section** & **Characters** & **Frequency** & **Ratio** (\%)** \\ \hline \multirow{4}{*}{Gender} & Male & 25 & 71.4 \\  & Female & 10 & 28.6 \\  & Total & 35 & 100 \\ \hline \multirow{4}{*}{Age} & 30s & 9 & 25.7 \\  & 40s & 16 & 45.7 \\  & 50s & 10 & 28.6 \\  & Total & 30 & 100 \\ \hline \multirow{4}{*}{Work experience in the related field} & 10–15 years & 8 & 22.9 \\  & 15–20 years & 21 & 60.0 \\  & Over 20 years & 6 & 17.1 \\  & Total & 35 & 100 \\ \hline \multirow{4}{*}{Professional area} & Blockchain technology & 7 & 20.0 \\  & 5G technology & 7 & 20.0 \\ \cline{1-1}  & Artificial intelligence & 6 & 17.1 \\ \cline{1-1}  & Manufacturing & 9 & 25.8 \\ \cline{1-1}  & Supply chain management & 6 & 17.1 \\ \cline{1-1}  & Total & 30 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Demographic data of the respondents involved in the study, including information such as the distribution of gender, age groups, and professional experience.
## 1 Introduction  
Over the past 20 years advances in technology have led to an impressive reduction in the cost of photovoltaic modules and other components, increasing efficiency and significantly improving both the reliability and the yield of the system, resulting in reduced electricity prices. These advances are associated with the rapid growth in the installed capacity of photovoltaic power plants. Photovoltaics has become one of the world's fastest growing energy segments. The cumulative PV capacity installed worldwide exceeded 760 \(\text{GW}_{\text{P}}\) in 2020 [1], of which over 140 \(\text{GW}_{\text{P}}\) was installed in 2020. The developments of the cumulative capacity and annual production in period 1992-2021 [2, 3] are shown in Figure 1. As shown, the cumulative installed capacity increased a thousand-fold between 2000 and 2020. In 2021, annual production (installed capacity) increased to more than 160 \(\text{GW}_{\text{P}}\) and in 2022 a further increase in annual production is expected. In this way, in 2022, the cumulative PV capacity installed worldwide would exceed the \(\text{TW}_{\text{P}}\) level.  
A very important part of photovoltaic systems are photovoltaic modules, which convert solar radiation into electricity. Their price, efficiency, and durability are important drivers for the development of photovoltaics as a future source of cheap and clean electricity. This paper discusses the influence of price, efficiency, and service life of PV modules on the levelized cost of energy (LCOE) (along with the availability of materials); the resulting trends in PV module technology; and some limits for the relevant technologies.
# Feasible Applicability of Deep Learning for Solid Detection in Concrete Wastewater: An Evaluation
## 1 Introduction  
Infrastructure construction requires a large amount of concrete. Wastewater is unavoidably produced in the concrete mixing station due to cleaning trucks and mixers [1]. Calcium oxide and calcium hydroxide in the concrete wastewater induce a high alkali content, which leads to serious soil alkalization and water pollution [2]. Therefore, determining the quantitative value of the wastewater solid content is critical for recycling what remains. The traditional and easy-to-operate exsiccation method is normally used to measure the pure mass of the solid in the wastewater [3]. But, it requires repeated steps such as weighing, drying, cooling, calculating, cleaning, etc., which are extremely time consuming. High-cost equipment and a large testing space are also essential to complete the whole exsisccation process. Moreover, it is hard to exploit real-time monitoring due to the interactive effects among the drying, cooling, and weighing processes [4]. [PERSON] et al. [5] exploited linear analysis to calculate, correct, and predict the quantitative value of the solid content by using experimental data in addition to using the traditional exsiccation method. However, the parameters of the linear analysis cannot be universally applied to wastewater from different mixing stations. [PERSON] et al. [6] further applied a refractometer to measure the solid content. The results show a smaller statistical dispersion, about a 0.8% deviation, compared with the standard value. More specifically, inspired by machine learning techniques, [PERSON] et al. [7] applied decision tree and neural network models to fit the results of concrete-like waste geopolymer powder content and found that the amount of quartz powder is the most critical for improving the mechanical properties. Such techniques have paved the way for rapid detection and provided the quantitative reference for the mass addition of concrete materials. Nevertheless, it is hard to find relevant research on timely solid detection when handling the concrete wastewater in mixingstations. Deep learning uses cameras and image processing algorithms to extract unique information, which can timely track the solid particles in the wastewater by analyzing the images or videos during sedimentation. Hence, this research focuses on the discussion and evaluation of deep learning applications for the real-time onsite detection of solid content in concrete wastewater.
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 3 Background
### Background on BB84
In a nutshell, these techniques address discrepancies between keys, which can result from eavesdroppers or noise. Since it is impossible to distinguish in advance between these types of errors, they are necessary to ensure final key security. Accordingly, if the number of discrepancies (in the compared bits) falls below a certain threshold, these techniques can be used to correct erroneous bits and subsequently reduce [PERSON]'s knowledge of the key to an arbitrarily small value. More detail follows:  
* Information reconciliation is a process of error correction performed between [PERSON] and [PERSON]'s keys to ensure that both keys are identical. * Privacy amplification is a method used to reduce and effectively eliminate [PERSON]'s partial information about [PERSON] and [PERSON]'s key.
## 3 System Models
### Network Model  
A number of sensor nodes are distributed in the area of interest. Distribution of nodes can be either uniform and random or fixed. They have the same resources, power and computational capabilities. A sensor node collects information about the target object, which falls within its sensing area and transmits this information to the sink node which has more resources, power and computational capabilities than the regular sensor node throughout the network. The sink node can be placed in any position in the network such as on one side of the network or in the center of the network. Sensing area for each node can be calculated as follows:  
\[\mathbf{R}_{\mathbf{area}}=\ \mathbf{\pi}\mathbf{r}^{2} \tag{1}\]  
where \(\mathbf{R}_{\mathbf{area}}\) is the range area and \(r\) is the transmitting range. In case of forwarding packets from one node to another, all nodes which satisfy the following equation  
\[\left(\mathbf{x}_{\mathbf{n}}-\ \mathbf{x}_{\mathbf{source}}\right)^{2}+\left(\mathbf{y}_{\mathbf{n}}- \ \mathbf{y}_{\mathbf{source}}\right)^{2}<\ r^{2} \tag{2}\]  
are considered neighbor nodes of the transmitting node, where \(\mathbf{x}_{\mathbf{source}}\) and \(\mathbf{y}_{\mathbf{source}}\) are coordinates of the source node and \(\mathbf{x}_{\mathbf{n}}\) and \(\mathbf{y}_{\mathbf{n}}\) are coordinates of the receiver node. Sensor nodes can locate their position by using one of the localization techniques [1] in the deployment stage of the network. There are many localization techniques in the literature that used by WSNs to provide each node in the network about its current location and neighboring nodes' locations. Many of the localization techniques provided in this literature can work just fine as an additive module to our framework. Each application has its own specific requirements such as the lifetime of the network or the maximum delay. The lifetime of the WSN can be divided into time intervals and each interval can have many subintervals. This could be adjusted to meet the application's requirements. All real and dummy packets are assumed to be encrypted using a shared key between the sender and receiver nodes and, therefore, the message payload is secure. Real and dummy packets are identical in terms of size and structure to avoid size correlation attack by the adversary. When a receiver node gets a packet, it can differentiate between real and fake packets by decrypting using the shared key.
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
## 5 Results
### Validation and Impact  
The successful reception of packets is less likely in a normal platoon than in a feudalistic platoon. The reason for this is first that it may not be possible to reach all vehicles of a platoon of a reasonable length with a single broadcast. As mentioned in Section 3, a distance below 50 m is required to reach a PRR above 90%. When taking into consideration an inter-vehicle distance of 10 m and a vehicle length of 16.5 m, which is typical for HGVs, a platoon consisting of three vehicles has already reached a length of almost 70 m. Packet forwarding may be necessary. Second, out-of-coverage or handover scenarios may lead to network partition. Again, packet forwarding may be necessary. Third, frequency reuse within the platoon is not possible resulting in a higher probability of packet collisions. The latter either requires the retransmission of the lost packets or compromises the safety of the platoon. Uncoordinated packet forwarding, i.e., the retransmission of received packets according to a hop limit, leads to an explosive rise in the transmitted data quantity. The following simple example illustrates the importance of refraining from packet forwarding and highlights the low overheads achieved by _Feudalistic Platooning_: We assume that all platooned vehicles have access to both ad hoc and cellular networks. Furthermore, we neglect the transmission of any platoon management data: vehicles send CA messages only. As elaborated in Table 2, 768 kB of CAM data are sent in the presented simulation scenario. Each vehicle is responsible for 64 kB of CAM data. Sending this as redundant data over both ad hoc and C-V2X network results in 1536 kB of CAM data. Once the overall CAM data of a vehicle are retransmitted at least 18 times (\(18\times 64\) kB \(=1152\) kB) over the duration of 300 s, the overall transferred data in the feudalistic platoon (2684 kB) are exceeded. Depending on the selected control policy and the length of the platoon, this may be quickly reached, for example, a platooned vehicle in [15] relies on data from the platoon leader and predecessor. If the CAM of a leader is forwarded by each vehicle, a platoon of 20 vehicles can be supported by the indicated data quantity (the head and tail of the platoon do not need to retransmit). With this approach, however, vehicles are only aware of their predecessor and the platoon leader. Lack of information about other platoon members may make platoon maneuvers more difficult. Similarly, the authors in [11] exchange vehicle control messages using 5G Mode-3, i.e., direct communication between the vehicles under network coverage. Furthermore, there, a complete picture of the composition of the platoon can only be obtained by packet forwarding.
# Space Weather Effects from Observations by Moscow University Cubesat Constellation
## 3 Variations of Electron Fluxes in the Outer Radiation Belt during Geomagnetic Disturbances  
The moderate geomagnetic activity was observed at the beginning of December 2021. As it can be seen from Figure 1, in which time courses of \(K_{\rm p}\), \(D_{\rm st}\), as well as solar wind velocity \(v_{\rm SW}\) and density \(\rho_{\rm SW}\) are presented, the moderate magnetic storm occurred on 1 December 2021. During this time, \(K_{\rm p}\) index reached values of about 4 while the \(D_{\rm st}\) variation was about \(-50\) nT. Approximately at the same time, the solar wind velocity increased to \(\sim\)600 km/s, and somewhat earlier, an increase in the solar wind density was observed, which indicates the arrival of a shock wave, which actually inspired the magnetic storm.  
The outer belt electron flux variations on different L-shells before, during, and after the magnetic storm are illustrated by curves in Figure 2. There we can see the dependence of electron fluxes \(J_{\rm e}\) on drift shell number (McIllwine parameter) \(L\). The \(J_{\rm e}\) values were obtained from count rates of electrons with energies more than 300 keV. There are counting rates of events detected in the plastic scintillator of the DeCoR instrument divided by its geometry factor. We use here the data from the DeCoR instrument installed on the DECART nanosatellite.  
As can be seen from the figure, the sub-relativistic electron flux in the outer radiation belt increases more than three times after the magnetic storm begins. This increase is approximately equal for all \(L\) values typical for the outer belt, i.e., from about 3.5 to 7. At the recovery phase, a slow flux increases near the maximum in \(L\) (\(L\sim 5\)-6) is observed, and some expansion of the belt edges also takes place, i.e., the inner edge shifts to \(L\sim 3\), and the outer edge--up to \(\sim\)9-10. By this, the fluxes near the inner edge of the belt possess irregular variations, i.e., first there is a decrease in the flux magnitude (see 4 December 2021), and then a gradual increase. After the recovery phase and the transition to a geo-magnetically quiet state, the electron flux inside the belt did not change much on average, but some compression of the outer edge was observed almost to the initial value of \(L\sim 7\).  
Figure 1: Parameters of solar wind and geomagnetic activity in late November–early December 2021. (**Top panel**) solar wind velocity \(v_{\rm SW}\) (red line), density \(\rho_{\rm SW}\) (blue line). (**Bottom panel**) \(K_{\rm p}\) (green line), \(D_{\rm st}\) (violet line) indices.
# Joint Client and Resource Optimization for Federated Learning in Wireless IoT Networks
## 2 System Model
### Network Model  
As depicted in Figure 1, we consider a FL-aided wireless IoT network, which consists of one base station (BS) and \(M\) IoT nodes. Let \(\mathcal{M}=\{1,\cdots,M\}\) denote the set of IoT nodes. A FL server is directly deployed at BS, which is responsible for broadcasting and aggregating FL model parameters. In the FL-aided wireless IoT network, a global learning model of interest is trained by the cooperation of the FL server and IoT nodes. In the training, each IoT node acts as a FL client, which collects data as the training sample for its local training. Let \(S_{i}=\{S_{1},\cdots,S_{l},\cdots,|S_{i}|\}\) denote the collected training data by IoT node \(i\), where \(S_{l}\) is the \(l\)th training sample of the data. Thus, the whole training data set can be expressed as \(S=\sum_{i\in\mathcal{M}}S_{i}\).
## 4 Limitations

Table 7: Comparison of results with benchmark schemes. devices with limited resources can be affected by various factors, including processing overhead and resource restrictions. Humans can find it challenging to understand model choices, especially when using complicated machine learning algorithms. Understanding how such results are achieved by the model is crucial to gain confidence and acceptance in real applications. However, if these constraints are tackled in future work, the suggested intrusion detection system might be more robust, flexible, and practical in real IoT contexts. The cluster-based classification (CBC) approach effectively detects known and novel attacks by combining feature selection, clustering, and classification. However, some limitations must be considered. Scalability remains challenging, especially when deploying the model across large-scale IoT networks with dynamic and heterogeneous data sources. While the model shows good accuracy in IoT environments with the used dataset, its real-time detection capabilities in live traffic scenarios require further validation. The ability to identify previously unseen attacks is supported by clustering and anomaly detection mechanisms, but the model's reliability depends on the diversity of training data. Moreover, the computational cost of clustering and classification processes, particularly during inference, raises concerns about practicality on resource-constrained IoT devices. Future work should explore lightweight versions of the CBC model, the optimization of real-time response, and thorough evaluation in real-world IoT settings to address these limitations effectively.