esa-sceva/satcom-chunk-collection huggingface dataset:
# Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network
## 2 Data Observation and Analysis
### Temporal Domain  
Figure 1 shows the trends in three different traffic flows over a 48 h period; from top to bottom, SMS, Call, and Internet. From the picture, we can clearly find that the three different traffics were subjected to a strong daily time change pattern. Basically, the traffic started to increase at approximately eight o'clock in the morning and then stayed above a very high level and started to fall at approximately eight o'clock in the evening. There were also significant differences between different traffics. The Internet traffic remained constant even at night, and Call and SMS were mostly concentrated during the day. These rules clearly correspond with the daily lives of people. Compared to daytime work and life, people make very few SMS messages and calls at night. However, the Internet not only provides contact needs, but automatic access equipment, entertainment, and other factors will lead to a large number of Internet accesses at night. Therefore, the day and night trend is relatively small compared to SMS and Call.  
Figure 2 is the dynamic graph of the daily total traffic in November 2013. The three types of traffic had obvious differences on working days and holidays, and the traffic on holidays was much lower than that on working days, which showed whether it was a working day that had an important impact on daily traffic. For example, the first day in the picture is an Italian legal holiday, and the second and third days are the weekend; the total traffic on these three days was significantly less than the next five working days.  
Comparing the changes in the three types of traffic on weekdays and holidays, we found that the gap of Call traffic on the two dates was considerable, and the working day traffic was generally close to twice the holiday traffic. SMS traffic trends were smaller than Call, but the gap was also large. The reason was obvious: users needed to communicate more during the working day. Similar to the statistical rule of Figure 1, the daily traffic trend of the Internet was smaller than SMS and Call.  
Figure 1: Hourly traffic change statistics.
## 3 Proposed Method
### Fusion and Classification Module  
As illustrated in the fusion classification module in Figure 1, the spatial feature vector \(V_{spa}\) and spectral feature vector \(V_{spe}\), post-global average pooling, are initially concatenated within this module. Subsequently, a convolution operation is employed for feature fusion, consolidating the separately collected spatial and spectral information into a space-spectral vector of dimension \(60\times 1\). Finally, classification is accomplished through fully connected layers, as depicted in the (12):  
\[Class=FC(\delta(CONV(Concat(V_{spa},V_{spe})))) \tag{12}\]  
where, \(Concat\) denotes the concatenation operation, \(CONV\) refers to the convolution operation, \(\delta\) signifies the activation function (where ReLU is utilized as the activation function), and \(FC\) represents the fully connected operation.
###### Abstract
A key concept in this field is ecosystem self-design, which allows ecosystems to self-regulate and adapt through strategic interventions to optimize their functionality [24; 25]. This method promotes sustainable coexistence between human activities, such as mining, and natural settings, including wetlands [23]. While ecological engineering principles, especially those applied to wetlands treating AMD, have been explored in previous research, their full potential and mechanisms remain incompletely understood [24; 26; 27]. Moreover, there is a pressing need for comprehensive studies on ecologically engineered wetlands that consider the entire ecosystem in their approach [27]. The Zaalklap-spruit wetland in South Africa, a valley bottom wetland severely impacted by AMD from an abandoned coal mine upstream, has demonstrated improved surface water quality following the implementation of ecologically engineered structures [8; 28]. The concrete structures were designed to decrease the surface water flow rates, increasing the residence time of contaminated water within the wetland. Although influent-effluent comparisons indicated noticeable improvements in water quality parameters, a deeper understanding of wetland dynamics necessitates investigation into the sources, pathways, and receptors of metals and sulfur compounds. This approach is essential for developing appropriate remediation designs and management strategies, ensuring the effectiveness of ecologically engineered wetland treatment, as shown in Figure 1[29]. An integrated source-pathway-receptor approach to wetland-based treatment systems is crucial for effectively designing, implementing, and managing ecologically engineered ecosystems. Considering the pivotal role of mining in South Africa's economy, environmental degradation is an almost inevitable consequence [8]. The imperative to restore equilibrium between resource utilization and its exploitation is critical. While ecological engineering has been under study since the 1960s, there remains a significant gap in the research concerning the efficacy and sustainability of wetland ecosystems engineered to mitigate the negative impacts of AMD. This research aims to delve into the dynamics within an ecologically engineered wetland affected by AMD, with several key objectives: (1) to evaluate the water quality improvement capabilities of the wetland; (2) to investigate the physical and chemical properties of the wetland's bottom sediment; (3) to examine the metal adsorption and absorption capacities of macrophtye roots and periphyton; (4) to explore the microbiological diversity within the sediment; and (5) to identify the sources, pathways, and receptors of metals and sulfur compounds in the wetland.
# Effect of Biochar Application Depth on Crop Productivity Under Tropical Rainfed Conditions
## 3 Results and Analysis
### Crop and Soil Moisture Dynamics  
From the measured fresh and dry weights of the yield components of the NERICA-4 rice cultivar, crop moisture content was determined using Equation (1) and plotted in Figure 6a,b. Also in Figure 6c is the soil moisture content calculated from soil samples taken from the treatment plots (see the materials and method section). Note that the trend lines in Figure 6 depict the average values across either the treatments or the yield components.  
For crop moisture content across the treatments (Figure 6a), moisture content was highest under TB (44.01%), followed by TC (37.14%), CK (36.87%), and then TA (33.75%). Also for crop moisture content across yield components (Figure 6b), it was highest for shoot (49.18%), followed by root (40.18%), the whole plant (37.94%), and then the grain (24.47%). Then for soil moisture content across the treatments (Figure 6c), it was highest under TA (15.79%), followed by TB (15.58%), CK (15.17%), and then TC (14.98%).  
For crop moisture content across treatment combinations, there was strong correlation (\(r>0.99\)) between every paired combination of the treatments. However, none of the correlations was significant at \(p=0.05\). For crop moisture content across yield component combinations, there was no correlation (\(r=0.45\)-0.47) between 2 of the paired yield component combinations (grain, root and grain, shoot) but there was correlation (\(r=0.67\)-0.87) between the other paired yield-component combinations. ANOVA analysis showed that except for the root, shoot and root, plant yield-component combinations, there were significant (\(p=005\)) differences in crop moisture content at the yield scale. Then at treatment  
Figure 5: (**a**) Weight of NERICA-4 rice plant along different treatments; (**b**) yield components. Treatments A, B, and C respectively denote the 10, 20, and 30 cm biochar application depths and then CK is the control.  
scale for soil moisture content, all the paired combinations were correlated (\(r\) = 0.73-0.99) but none was significant at \(p\) = 0.05; except for the TA, TC (\(p\) = 0.02), and TB, CK (\(p\) = 0.03) treatment combinations.  
While the differences in both the crop and soil moisture contents was attributable to treatment and genetic factors, also field conditions (that were not part of the treatment) possibly contributed to the differences. For instance, the separation of the treatment plots by bamboo canes caused water logging in TA and CK treatments during rainfall events. Also mixing biochar with dug-out soil caused mounting after backfilling under TC (the treatment with the highest depth, 30 cm), limiting infiltration during rainfall events. These field conditions in combination with treatment effects and genetic factors contributed to the differences in both the soil and crop moisture contents in the study. This necessitated screening for the three factors (genetic, treatment, and field condition) with the highest impact on the observed discrepancies.
# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
###### Abstract  
Web service composition requires high levels of integration and reliability of the services involved in its operation, which must meet specific quality criteria to ensure their proper execution and deployment. The discovery and selection of web services currently face optimization problems. Many services might satisfy a requirement with similar quality criteria. Because of this, software developers have to choose the most appropriate services for a given composition, complicated by the rapid increase in providers and services available in the cloud. Service composition also implies coupling according to a composition flow and non-functional requirement criteria. Such requirements make selection and composition a complex task not previously solved in the literature. This paper presents Ar_WSDS, a computational approach for web services discovery and selection in cloud environments, which bases its implementation on the brain's pattern recognition systematic functioning. This process allows classifying web services through recognition modules created dynamically based on their quality parameters, resulting in a set of web services suitable for a web service composition. This approach allows a solution to the selection problem using less complex tasks. This paper introduces an architectural and procedural definition that provides the web service description with a pattern to recognize and select services using different recognition levels. We simulated our approach and evaluated it using a dataset from the QWS project that offers a set of quality criteria collected from different providers. The web services are recognized and classified using different quality criteria for the composition and each of their services. The results demonstrate the effectiveness of the discovery and selection process compared to other approaches. Furthermore, Ar_WSDS allows us to recognize and filter out web services with ambiguity and similarity in their provider information, a process that minimizes the discovery space for services.  
web service composition; cloud computing; pattern recognition +
Footnote †: 2021: 11, 8092. [[https://doi.org/10.3390/app11178092](https://doi.org/10.3390/app11178092)]([https://doi.org/10.3390/app11178092](https://doi.org/10.3390/app11178092))
## 6 Computational Experiments
### Implementation and Instances  
This section aims to evaluate the performance of the proposed meta-heuristics on several randomly generated instances. First, the multi-objective mathematical model is implemented and solved using Lexicographic Goal Programming optimization in IBM CPLEX solver (Version 12.9). A time limit of one hour (3600s) is set for the solver. The heuristic (\(H_{0}\)) and the two meta-heuristics (MO-VNSSA and MO-VNSSTS) are developed in C++ and all the experiments are performed on an Intel\({}^{(R)}\) Core\({}^{(TM)}\) i3 processor with 4 GB of RAM. The tests are performed in 5 replications for each instance. The average value is presented. The proposed algorithms are tested on a set of small and large instances randomly generated by varying several parameters. The values of the parameters are summarized in Table 1. The tuning of the meta-heuristics parameters is presented in Table 2.
## 7 Proposed Extensions to Bio-2 FA-IoD
### Sybil-Free Pseudonym Management for Enhanced Anonymity
#### 7.2.2 Security Implications  
This pseudonym management system would provide enhanced **user anonymity** and **unlinkability** across different IoD service contexts. An adversary would be computationally unable to link an operator's activities in one context to their activities in another, thus preventing comprehensive profiling. Furthermore, the \"Sybil-free\" property offers strong resistance against **Sybil attacks**, a critical threat to reputation systems and privacy in distributed environments. The ability to detect and potentially revoke misbehaving pseudonyms ensures accountability while preserving anonymity for compliant users.
# Review of Artificial Intelligence Adversarial Attack and Defense Technologies
## 3 Adversarial Attacks
### Training Stage Adversarial Attacks  
As shown in Section 2.2.1, the adversarial capabilities in the training stage can be divided as data injection, data modification, and logical corruption. Corresponding to different capabilities, training stage adversarial attacks can generally be carried out in three ways:  
* **Modify Training Dataset.** [PERSON] et al. [7] firstly proposed the term poisoning attacks, a common adversarial attack method. They changed the original distribution of training data by modifying or deleting training data or injecting adversarial samples, in order to make the learning algorithm logically changed. [PERSON] et al. [60] showed that, when the model's prediction error is smaller than \(\varepsilon\), the probability \(b\) of maximum tolerance to modify the training dataset should satisfy: \[b\leq\frac{\varepsilon}{\varepsilon+1}\] (2)
* **Label Manipulation.** If an adversary only has the capabilities to modify the training labels with some or all knowledge of the target model, he needs to find the most vulnerable labels. Perturbing labels randomly, one of the most basic methods to modify labels, refers to select a label from the random distribution as the label of training data. [PERSON] et al. [8] showed that randomly flipping 40% of training labels is enough to reduce the performance of classifiers using SVMs.
* **Input Feature Manipulation.** In this case, the adversaries are powerful enough to manipulate labels as well as the input features of training points analyzed by the learning algorithm. This scenario also assumes that the adversaries have knowledge of the learning algorithm. [PERSON] et al. [11] and [PERSON] et al. [13] showed the adversaries injected malicious data generated carefully to change the distribution of training dataset. Therefore, the decision boundary of the trained model changes accordingly, which reduces the accuracy of the model in the test stage, even, leads the model to output the specified misclassification labels. [PERSON] et al. [9] showed that inserting malicious points into a training dataset can gradually change the decision boundary of an anomaly detection classifier. Similar works have been done in articles [10; 12].
# Satellite IoT Edge Intelligent Computing: A Research on Architecture
## 3 Satellite IoT Edge Intelligent Computing Architecture
### Satellite IoT Edge Computing
Network slicing can provide independent network instances by satisfying the differentiated requirements of different service qualities, such as bandwidth and delay. At the same time, it can enhance the flexibility and adaptability of the network.
## 3 Results
### Summary of Results
#### 3.5.3 Process 3  
The results for this system are similar to those obtained for the first two systems. The performance boundary is even more apparent here. For disturbance rejection, in terms of TV, MPC gave a value of 1 for all values of \(r_{w}\), \(\frac{\theta}{\tau}\) and \(\zeta\). The same result was obtained in setpoint response for \(1\leq r_{w}\leq 10\). For \(0.01\leq r_{w}\leq 0.1\), MPC had a TV of 1 when \(\frac{\theta}{\tau}<1\) for all values of \(\zeta\). In terms of IAE, MPC had the best performance in setpoint response when \(\frac{\theta}{\tau}\geq 1\) for \(0.01\leq r_{w}\leq 0.1\) and \(\frac{\theta}{\tau}\geq 3\) for \(1\leq r_{w}\leq 10\). For disturbance rejection, MPC had the best IAE when \(\frac{\theta}{\tau}\geq 1\) for all tunings. Hence, MPC could be said to perform better in most cases when \(\frac{\theta}{\tau}\geq 3\) overall values of values. The trend for lower values \(\frac{\theta}{\tau}\) is similar to the first two processes, with MPC performing relatively better as damping decreases. This boundary is \(\frac{\theta}{\tau}\geq 1\) for aggressively tuned MPC, i.e., \(0.01\leq r_{w}\leq 0.1\).
# Performance Analysis of D2D Communication with Retransmission Mechanism in Cellular Networks
## 7 Conclusions  
This paper focused on the performance analysis of D2D communication in underlay mode in cellular networks. Firstly, by adopting queuing theory and stochastic geometry, we developed a spatiotemporal traffic model to model a retransmission mechanism for D2D underlay communication. Considering the influence of interference among D2D links and the channel condition, we described the dynamic traffic model of a D2D receiver as a queuing model with dynamic data arrival and a finite buffer. Following the SW-ARQ protocol, we modeled the D2D communication model with a retransmission strategy as a feedback queuing model and formulated it as a Markov chain to analyze the D2D queue length state of the packets in the buffer, and the closed-form expression of the steady-state distribution was obtained. Then, the expressions of the performance metrics, namely the average queue length, mean throughput, average packet delay, and packet dropping probability, were obtained. Simulation results showed the validity and rationality of the theoretical analysis. Moreover, simulation results indicated that the performance evaluation method using the feedback queuing model had practical significance in D2D heterogeneous networks. In future work, we can apply the queuing model and analytical approach presented in this paper to conducting performance studies on D2D-assisted wireless caching networks. By employing the queuing theory and stochastic geometry, we can build a spatiotemporal model to analyze the delay and obtain the probability in caching networks simultaneously. We can also study the performance of the go-back-N (GBN-ARQ) and selective-repeat (SR-ARQ) protocols in multi-hop communication. Specially, we will concentrate on using more realistic assumptions to build a dynamic model in further research. We hope our work can provide a more accurate theoretical basis for the design of the actual system.  
[PERSON] and [PERSON] conceived of and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] contributed materials/analysis tools; [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
The work is supported by the Natural Science Foundation of China (61971239, 61631020), the Innovation Project for Postgraduates of Jiangsu Province (KYCX19_0887), the Youth Foundation of Anhui Polytechnic University (2014 YQ40), the General Research Project of Anhui Higher Education Promotion Plan (Grant TSKJ2015B18, KZ00215021, KZ00215022), and the High-level Introduction of Talent Scientific Research Start-up Fund of Jiangsu Police Institute (2019).  
The authors declare no conflict of interest.
## 6 Results and Discussion
### Case 4. Complex Partial Shading Condition
It is noted that the proposed technique presents the lowest settling time that decreases the power losses and enhances the efficiency of the PV energy systems. In CPS conditions the convergence time of PSO, DFO, InC, hybrid, CS, FA, and ACO techniques are 1.31 s, 0.69 s, 0.39 s, 0.41 s, 0.73 s, 0.52 s, and 0.59 s, appropriately. Power of proposed hybrid and all previous PSO, DFO, InC, hybrid, CS, FA, and ACO techniques are 1246 W, 1248 W, 1094 W, 1249.8 W, 1243 W, 1157 W, and 1247.1 W, respectively. The hybrid algorithm gives the highest power among all the techniques. From Table 3, it is observed that it has the lowest convergence and settling time. The proposed algorithm tracks the GM efficiently and gives maximum efficiency of 99.93%, 99.88%, 99.92%, and 99.98% in all operating conditions due to their two-stage running phenomena.
## 3 Results
### pH and VFA  
The pH was fairly stable across the tested OLR range (1.0 gCOD/dm\({}^{3}\).d to 4.0 gCOD/dm\({}^{3}\).d). The pH fell within the narrow range of 7.74 \(\pm\) 0.14 pH to 7.37 \(\pm\) 0.04 pH in both stages and series (Figure 4a). Significantly lower pH was noted in V5, with values of 6.91 \(\pm\) 0.08 pH for S1 SER1 and up to 6.96 \(\pm\) 0.14 pH in S1 SER2 (Figure 4a). All series of S2 had similar pH at 6.95 \(\pm\) 0.13 (Figure 4a). An increase in OLR to 6.0 gCOD/dm\({}^{3}\).d led to a significant decrease in the pH. The values in this variant fell within the narrow range of 6.06 \(\pm\) 0.05 pH to 6.26 \(\pm\) 0.21 pH, depending on the stage and series (Figure 4a).  
Figure 4: Changes in pH values (**a**) and concentration of volatile fatty acids (VFA) (**b**) in the digesters across experimental variants.  
Figure 3: Changes in effluent TOC (**a**) and TOC removal rate (**b**) across experimental variants.  
The lowest values regarding VFA levels in the effluent were noted for S2V1 and S2V2. In the former, the final concentration was 0.34 \(\pm\) 0.03 g/dm\({}^{3}\) in SER2 (55 \({}^{\circ}\)C) and 0.37 \(\pm\) 0.04 g/dm\({}^{3}\) in SER1 (35 \({}^{\circ}\)C) (Figure 4b). The corresponding values for S2V2 were 0.32 \(\pm\) 0.03 g/dm\({}^{3}\) (SER2) and 0.39 \(\pm\) 0.06 g/dm\({}^{3}\) (SER1) (Figure 4b). V3 showed a significant rise in effluent VFA. The final concentrations in S1 SER1 and S2 SER1 were 0.55 \(\pm\) 0.04 g/dm\({}^{3}\) and 0.47 \(\pm\) 0.03 g/dm\({}^{3}\), respectively (Figure 4b). Significant differences between the two stages were also noted for SER2 (55 \({}^{\circ}\)C). S1 had effluent VFA levels of 0.55 \(\pm\) 0.04 g/dm\({}^{3}\), compared to the 0.47 \(\pm\) 0.05 g/dm\({}^{3}\) noted in S2 (Figure 4b). V3 had significantly higher VFA levels--0.83 \(\pm\) 0.05 g/dm\({}^{3}\) in S1 SER1 and 0.85 \(\pm\) 0.06 g/dm\({}^{3}\) in S1 SER2 (Figure 4b). The MW heating (S2) significantly decreased the VFA levels, with 0.72 \(\pm\) 0.06 g/dm\({}^{3}\) in SER1 (35 \({}^{\circ}\)C) and 0.67 \(\pm\) 0.05 g/dm\({}^{3}\) in SER2 (Figure 4b). Further increases in effluent VFA were noted for variant V5 (5.0 gCOD/dm\({}^{3}\)). S1 SER1 (35 \({}^{\circ}\)C) and S2 SER1 produced 1.11 \(\pm\) 0.02 g/dm\({}^{3}\) and 1.07 \(\pm\) 0.05 g/dm\({}^{3}\), respectively (Figure 4b). By comparison, the values for SER2 (55 \({}^{\circ}\)C) were 1.09 \(\pm\) 0.02 g/dm\({}^{3}\) (S1) and 1.06 \(\pm\) 0.05 g/dm\({}^{3}\) (S2) (Figure 4b). The OLR 6.0 gCOD/dm\({}^{3}\) groups exhibited even higher concentrations of VFAs in the effluent, up to a level of 1.34 \(\pm\) 0.11g/dm\({}^{3}\) (S2 SER1) (Figure 4b).
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 9 Challenges in the Hybrid Deep Learning Model for Brain Stroke Detection and Prediction
### Limited Availability of Medical Datasets  
High-quality medical datasets are difficult to get due to privacy concerns and rigorous rules on patient data exchange. Many hospitals and institutes have restrictions on making patient information publicly available, which results in a scarcity of varied and large-scale datasets for training DL models. This constraint reduces the capacity to create strong and generalizable models. Furthermore, publicly accessible stroke datasets may be obsolete, missing values, or lacking essential properties, rendering them unsuitable for real-world clinical applications. Collaborations with medical institutions, as well as the usage of federated learning methodologies, may help to solve this difficulty.