esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Collaborative Computing Strategies  
Collaborative computing strategies, built upon optimized resource scheduling, aim to enhance network computing capabilities and service quality in satellite networks and STIN. With the growth of the computational power of satellite platforms, efficient resource sharing through collaboration among different satellite nodes becomes essential. Approaches have been employed to address challenges related to task processing and resource sharing, such as edge computing, distributed computing, and cloud computing. Studies demonstrate that satellite collaboration can significantly boost computing capacity and task processing efficiency without adding system load. For example, [PERSON] et al. [20] introduced the SATECN architecture, which facilitates cloud-edge-device collaboration for effective computing resource allocation. Similarly, [PERSON] et al. [21] designed a collaborative satellite-terrestrial network architecture with a central-edge multi-agent collaboration model, enhancing task offloading efficiency.  
To further enhance flexibility and scalability, distributed computing models have been developed [22; 23; 24]. Particularly, [PERSON] et al. [22] presented a distributed optimization model using the alternating direction method of multipliers (ADMMs), which alleviates computational burdens through collaborative calculations among multiple satellites. In a similar vein, [PERSON] et al. [23] explored a collaborative computation offloading and resource allocation framework, enabling tasks from mobile devices (MDs) to be partially processed at terminals, edge nodes (ENs), and cloud centers (CCs).  
However, current collaborative computing strategies primarily focus on static environments, frequently overlooking dynamic changes such as node mobility and network topology variations. This limitation underscores the need for more robust and adaptable strategies, which will be further addressed in the following section on task offloading methods.
## 5 Conclusions  
A fifth-order wideband Chebyshev bandpass filter in groove gap waveguide (GGW) technology is proposed for the first time in this paper. This filter is conceived from metallic pins, acting as impedance inverters. The metallic pins are adequately centered, distributed, and sized along the groove area of the GGW, according to a Chebyshev stepped impedance technique. The bandpass response of this filter is obtained by combining the high-pass behavior of the GGW with the low-pass characteristics of the metallic pins. This design technique is simple and allows suitable implementations of high-performance wideband filters using a computer numerical control (CNC) machining or an emerging three-dimensional (3D) printing technique, thus providing low-cost and low-weight prototypes. Three prototypes of the proposed filter have been fabricated: one using a CNC milling machine and the other two by means of a stereolithography-based 3D printer and a photopolymer resin. One of the two resin-based prototypes has been metallized using an expensive high-vacuum silver evaporation technique, while the other has been coated by means of a cheap conductive paint spray system. The measured frequency responses of these prototypes have shown good agreement with EM simulations. The proposed filter is less sensitive to mechanical tolerance since it is not based on high-\(Q\) resonators. The three prototypes have exhibited high-performance responses and a reduced size compared to other GGW bandpass filters. These wideband GGW filter prototypes are expected to be useful in future emerging communications systems. [PERSON] made substantial contributions to analysis and design of the devices and performed the numerical simulations. [PERSON] and [PERSON] also contributed to filter design. In addition, [PERSON] carried out the measurements of the prototypes. [PERSON] and [PERSON] carried out the metallization of the resin-based prototypes. All authors participated in the writing of the manuscript. All authors have read and agreed to the published version of the manuscript. The authors gratefully acknowledge financial support from Agencia Estatal de Investigacion (AEI) and Fundacion Seneca Region de Murcia of Spain for their financial support (grants no. : PID2019-103982 RB-C42/AEI/10.13039/501100011033, TED2021-129196B-C42 and 22076/PI/22). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.
## 2 Methodology
### Step 1: Data Acquisition, Characterization and Preprocessing  
Development of a consistent prediction model includes four main steps as part of a Knowledge Data Discovery (KDD) process: data acquisition, preprocessing, model training and validation.  
Regarding data acquisition, home electricity consumption for 4404 households with fix tariffs (not subject to dynamic time of use) were acquired from the Low Carbon London project led by UK Power Networks [25]. The dataset includes smart meter readings (Landis y Gyr (L + G) E470) taken at half hourly intervals for a period between November 2011 and February 2014. All selected households depended solely on electricity, excluding those with gas, prepaid consumption, micro-generation and in vulnerable situations [30].  
The customers in the trial were recruited as a balanced sample representative of the Greater London population, based on their CACI Acorn group [31]. Acorn is a segmentation tool that categorizes UK population in 17 groups (A to Q from higher to lower social status) based on post code and demographic variables (life stiles, behaviours and  
Figure 1: Main workflow proposed: Data acquisition and preprocessing, Energy ediciency model and Validation of results.  
other socio-economic variables obtained from public and private sources) and statistical algorithms, created to help the public and private sector to develop business strategies.  
The meteorological data were collected at hourly basis from a local weather station through website Dark-Sky [32], including the following parameters: temperature (\({}^{\circ}\)C); dew point (\({}^{\circ}\)C); humidity (0-1 decimal percentage); UV index; wind speed (mph); type of precipitation (rain or snow) and summary of the weather in text format (partially/mostly cloudy/cloudy, foggy, windy, clear, and all possible combinations).  
Finally, school calendar, bank holidays, time of day and day type, variables commonly used for their relation to occupancy [33; 34; 35], were also considered to enhance the performance in the predictions. Furthermore, as the number of costumers during the study is not constant, either because of problems with the smart meters functioning or their staggered entry in the project, an additional variable related to the number of active smart meters was included to correct the predictions.  
Data preprocessing consisted of a simple transformation of text and calendar variables into categorical classes to fit into the models and a pre-selection of the attributes based on their co-correlation, because the selected models, explained in the following subsection, have no need for elimination of outliers or additional data transformation [36]. Half hourly energy consumption data were grouped to hourly level to meet meteorological registers. Data rejection and transformation has been kept to minimal to provide the models with the most information possible, but number of active smart-meters was introduced as additional variable to diminish the potential bias associated to the number of connected clients at each moment.
# GPRS Sensor Node Battery Life Span Prediction Based on Received Signal Quality: Experimental Study
## 5 Conclusions and Future Work  
The results from our experiments show the GSM/GPRS sensor node consumes current in the mA range; this current is not small in WSN or IoT applications. However, in an area like Kigali, Rwanda a country with a lot of hills where the geographical structure does not allow the line of sight communication, building a network using low power sensor nodes like Lora or Sigfox will cost a lot due to a lot of gateways. So, in applications where the line of sight is difficult to achieve and where the GSM network is already in place, GPRS sensor nodes can be used and the cost for building a network will not be expensive as the GSM network is already in place. The quantitative model developed in this paper will help sensor nodes supplied with batteries as it is possible to know how long a battery will last if the information about the signal quality is known and this will finally help to predict when a battery can be replaced. This work has been completed using GSM module. However, we recommend  
Figure 20: Mathematical model.  
for the future work that a model like the one found in Equation (11) can be found for other modules such as LoRa and Sigfox using the same procedures.  
The ideas presented in this work are a product of contributions by all the authors. The conceptualization of the idea and the development of the manuscript was done by author [PERSON] in consultation with the supervision team comprising authors [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] The supervision team played a key role in providing the needed advise and direction throughout the development process of the manuscript. Data curation, Formal analysis, Investigation, Methodology, and Software was done by [PERSON] with the direct supervision of [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the African Center of Excellence in Internet of Things (ACEIoT), University of Rwanda.  
The authors declare no conflict of interest.The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IoT & Internet of Things \\ GSM & Global System for Mobile Communication \\ GPRS & General Packet Radio Services \\ RSSI & Received Signal Strength Indicator \\ dBm & Decibel \\ WSN & Wireless Sensor Network \\ APM & Advanced Process Monitor \\ LabVIEW & Laboratory Virtual Instrumentation Engineering Workbench \\ BTS & Base Transceiver Station \\ RTM & Remote Temperature Monitor \\ LEACH & Low-Energy Adaptive Clustering Hierarchy \\ GPS & Global Positioning Satellite \\ SNR & Signal to Noise Ratio \\ WIFI & Wireless Fidelity \\ DC & Direct Current \\ S & Second \\ mA & Milli Amperes \\ DB & Database \\ MySQL & Structured Query Language \\ AT & Attention \\ TA & Terminal Adaptor \\ No & Serial Number \\ NTC & Negative Temperature Coefficient \\ LoRa & Long Range \\ \end{tabular}
## 5 Experimental Results
### Baseline Experiment Comparison
Even in the face of label distribution drift, the model relies on dual embedding representations and attention guidance mechanisms to effectively alleviate the deviation problem caused by the difference in the proportion of malicious traffic in the training set and the test environmental imbalance. In terms of accuracy improvement, EFTransformer significantly outperforms traditional ML detection models, with an overall accuracy of 95.26%, an increase of 1.55% over the best-performing baseline model Enhanced-MLP, and an increase of 2.43% and 1.78% over FCM-SVM and CIC-RF, respectively. Secondly, EFTransformer's performance in the two core indicators of FPR and FNR is particularly outstanding. Its FPR is 6.19%, which is at least 4% lower than that of CIC-RF and FCM-SVM, and indicates that its false alarm control ability has been greatly improved. At the same time, its FNR is 5.85%, which is much lower than other models, indicating that EFTransformer is less likely to miss real attack traffic and has stronger robustness. In summary, EFTransformer not only leads in detection accuracy but also shows obvious advantages in reducing FPR and FNR, reflecting its practical application value in encrypted malicious traffic scenarios. The EFTransformer model achieved the highest accuracy of 95.26% and the lowest FPR and FNR in the experimental comparison, demonstrating that the dual-channel embedding module effectively captures the global semantics of traffic patterns through contextual co-occurrence relationships, thereby enriching the semantic representation of network traffic. It also confirms that the parallel encoding module enables semantic interactions across feature dimensions; the multi-head attention mechanism enhances deep dependencies between features, and the attention pooling mechanism, as a dynamic aggregation  
Figure 8: Comparison of the ROC for the baseline model. strategy, improves the model's ability to focus on key behavioral features. This lays a solid foundation for future research. In addition to improving detection accuracy, further work will explore lightweight multi-channel architectures and adaptive attention mechanisms to enhance the model's deployment efficiency and real-time detection capabilities in edge computing environments. The detection accuracy of 95.26% indicates that EFTransformer can already identify most potential encrypted malicious behavior patterns, demonstrating strong practical value and stability in real-world applications. It reflects the model's high reliability and decision-making capability when dealing with complex traffic scenarios. However, less than 5% of attack traffic remains inaccurately detected, suggesting that there is still room for improvement in handling extreme or stealthy threats. Future research will continue to optimize the model to achieve higher detection coverage and accuracy.
## 3 Results
### Optimal Model Stability  
First, we changed the data structure to build a new model. The results of the model preference in the previous step show that SVM is the best model among the three runoff simulation models. By changing the data structure, the new SVM model was employed with twelve bunches of streamflow data after naming it SVM (M). The new model prediction results are shown in Figure 13.  
The stability of the model was measured by measuring the degree of variation in five indicators. The degrees of model variability derived by the IHA-RVA method are shown in Table 5. From the table, we can find that five indicators have changed simultaneously. Among them, two indicators show that the model constructed with the new data structure has a better prediction, namely _MAPE_ and _QR_, where _MAPE_ is reduced by 11.52% from the original 102.18% and _QR_ is changed from 0.54 to 0.56. The other three indicators show that the original data structure can perform a better runoff prediction, and the initial SVM model has a better _NSE_ value than the new one. The _NSE_ value of the initial SVM model is 0.08 higher than that of the new data, while both _RMSE_ and _MAE_ are lower than that of the original model. Overall, the original model structure facilitates a better runoff prediction. In addition, the variability \(D_{i}\) of all five indicators is less than 33%, and the overall variability \(D_{0}\) of the model is only 10.52%, indicating that the model is of low variability (a highly stable state).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **SVM Model** & **ENN Model** & **MM Model** \\ \hline \(M_{R}\) & 0.53 & 0.00 & 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the combined assessment of the three models.  
Figure 13: Prediction results of SVM (M) models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **NSE** & _MAPE_ & _RMSE_ & _MAE_ & _QR_ \\ \hline SVM model & 2 & 1 & 2 & 1 & 1 \\ Elman model & 3 & 3 & 3 & 3 & 3 \\ MMM model & 1 & 2 & 1 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The ranking of the three models under five indicators (\(r_{i}\)).
## 2 Methods
### Methodological Framework  
The methodological framework can be visualized in graphical format (Figure 2). The calculation of 3-month SPEI was carried out for the time period 1902-2015 using precipitation and Potential Evaptranspiration (PET) data from the Climate Research Unit (CRU) dataset. These gridded datasets were then used to acquire 3-month SPEI data from Global SPEI Database, SPEIbase (version 2.5.1), for 1902-2015 at a resolution of 0.5\({}^{\circ}\)[30]. The Spatial \"K\"luster Analysis using Tree Edge Removal (SKATER) method was then applied to all of the 327 grid cells in Pakistan. The country was divided into five zones according to the similarity of SPEI in each cell, keeping into consideration the spatial contiguity of the dataset. For the spatiotemporal analysis, mean drought count, duration and severity were calculated for each grid cell. This was followed by applying the run theory to each of the five zones to characterize the meteorological droughts that occurred from 1902-2015. Droughts were  
Figure 1: Elevation map of Pakistan.  
characterized in terms of duration, severity, intensity, and peak. Trend analyses were then performed on each of the zones' SPEI and on drought characteristics using the Modified Mann-Kendall test.
# An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band
###### Abstract  
Carrier aggregation (CA) technology in the Fifth-Generation (5G) network can be considered as a major feature for high-rate data services, which can be combined with the concept of 5G in unlicensed band (5G-U) for data offloading to further enhance the efficiency of the scarce licensed spectral resource. However, the introduction of cellular services in an unlicensed spectrum can force unlicensed band users to silent mode. Meanwhile, the coexistence among different radio access technologies operating on the same frequency band is a complex issue, especially considering the requirement diversity of quality of service (QoS) in networking. In this paper, we propose an optimal resource allocation mechanism to maximize system performance satisfaction for multiple types of services with different QoS requirements in the 5G-U network with a CA feature to address the challenge. Additionally, the performance of services in Wi-Fi system is protected by reserving the minimum spectrum resource when offloading for the 5G-U network. Analytical results on the mean ergodic achievable rate of Wi-Fi services and the mean packet delay of 5G services are provided to maximize the system performance satisfaction. Numerical results demonstrate the effectiveness of the proposed algorithm, wherein optimal spectrum and power allocation can contribute to the maximum system satisfaction in a 5G-U network with a CA feature.  
5G-U; carrier aggregation; ergodic achievable rate; MAC mechanism; mean packet delay; optimal resources allocation; QoS; system satisfaction +
Footnote †: journal: Applied  
**Clication:** [PERSON]; [PERSON]; [PERSON]; [PERSON] An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band. _Appl. Sci._**2023**, _13_, 10638. [[https://doi.org/10.3390/app131910638](https://doi.org/10.3390/app131910638)]([https://doi.org/10.3390/app131910638](https://doi.org/10.3390/app131910638))
###### Abstract  
Accurate and reliable trajectory prediction is crucial for autonomous vehicles to achieve safe and efficient operation. Vehicles perceive the historical trajectories of moving objects and make predictions of behavioral intentions for a future period of time. With the predicted trajectories of moving objects such as obstacle vehicles, pedestrians, and non-motorized vehicles as inputs, self-driving vehicles can make more rational driving decisions and plan more reasonable and safe vehicle motion behaviors. However, due to traffic environments such as intersection scenes with highly interdependent and dynamic attributes, the task of motion anticipation becomes challenging. Existing works focus on the mutual relationships among vehicles while ignoring other potential essential interactions such as vehicle-traffic rules. These studies have not yet deeply explored the intensive learning of interactions between multi-agents, which may result in evaluation deviations. Aiming to meet these issues, we have designed a novel framework, namely trajectory prediction with attention-based spatial-temporal graph convolutional networks (TPASTGCN). In our proposal, the multi-agent interaction mechanisms, including vehicle-vehicle and vehicle-traffic rules, are meticulously highlighted and integrated into one homogeneous graph by transferring the time-series data of traffic lights into the spatial-temporal domains. Through integrating the attention mechanism into the adjacency matrix, we effectively learn the different strengths of interactive association and improve the model's ability to capture critical features. Simultaneously, we construct a hierarchical structure employing the spatial GCN and temporal GCN to extract the spatial dependencies of traffic networks. Profiting from the gated recurrent unit (GRU), the scene context in temporal dimensions is further attained and enhanced with the encoder. In such a way, the GCN and GRU networks are fused as a features extractor module in the proposed framework. Finally, the future potential trajectories generation tasks are performed by another GRU network. Experiments on real-world datasets demonstrate the superior performance of the scheme compared with several baselines.  
autonomous vehicle; trajectory prediction; intersection scene; graph convolutional networks; NGSIM +
Footnote †: journal: Applied sciences
## 3 Aeroelasticity and Aerodynamics of Cable-Supported Bridges
### Vortex-Induced Vibration
#### 3.2.4 Summary  
Wind tunnel tests have shown great performance in reproducing VIVs on small-scaled models. However, as VIVs are sensitive to the Reynolds number and the vortex shedding effect, the use of these small-scaled models will compromise the validity of the findings from two aspects. Firstly, small-scaled models will require a high testing wind velocity in order to achieve consistency with the Reynolds number of the full-scale bridge prototype, which is challenging for the wind tunnel. Secondly, in the investigations of the effect of different VIV suppression strategies, controlling the boundary layer developed at the attachment of the small-scaled model is difficult and might lead to larger errors as the scale decreases. One of the solutions of such issues is to use large-scaled models but the cost is tremendously higher than regular tests, which makes the other option, CFD simulations, a more feasible approach to investigate VIVs.  
The state-of-the-art simulations have revealed that the critical velocities and the amplitudes of VIVs are sensitive to the angle of attack, the Reynolds number, the intensity and length scale of the incidence turbulence, the spanwise domain size and the spanwisediscretisation. It is recommended that further studies of VIVs using CFD simulations include these factors so that findings can be extended for more general applicability. Additionally, most studies in this area have adopted free oscillation simulations because the identification process of the critical range of velocities is more straightforward. However, simulating free oscillating objects using 3D LES or DES is still computationally demanding. Therefore, many studies either chose to perform 2D simulations or conduct URANS simulations. The former usually overestimates the VIV amplitude whereas the latter can lead to errors related to oversimplification of fluctuations. Meanwhile, recent studies have demonstrated the feasibility of using forced oscillation simulations, a computationally more affordable method, to identify the amplitudes and critical velocities of VIVs. Clearly, further improvements and use of forced oscillation simulations in the study of VIVs will be very welcome.