esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Tensile Test  
The requirements for the tensile test of BFRP laminates were conducted in accordance with ASTM D3039 [41]. The rectangular tensile specimens with the dimensions of 250 \(\times\) 15 \(\times\) \(h\) mm (shown in Figure 1b) were cut from the fabricated BFRP laminates, which were soaked in deionized water and alkaline solution (pH = 13) at 60 \({}^{\circ}\)C last up to 180 days. As shown in Figure 1b, aluminum end-tabs were used in two ends to avoid the premature damage of the specimen during the tensile test. According to ASTM D3039 [41], the monotonic tensile test was conducted using a universal machine (Model WDE-200E, Jinan Gold Testing Machines Inc., Jinan, China) through displacement control with a displacement speed of 2 mm/min. The tensile stress \(\sigma\) could be calculated according to the Equation (2) and the strain was measured using an extensometer with a gauge length (Model Y50/10, Changchun Sanjing Test Instrument Co., Ltd., Changchun, China) of 50 mm in the test, as shown in  
Figure 1: Test specimens of BFRP laminates. (**a**) water absorption test and (**b**) tensile test.  
Figure 2. It should be noted that the acquired tensile stress was nominal tensile stress, which is more convenient to apply in practice engineering. Five specimens of each thickness were tested at specific duration, the average values and standard deviation of tensile strength retention and tensile elastic modulus were respectively calculated.  
\[\sigma=\frac{F}{A} \tag{2}\]  
where \(F\) represents the measured tensile force. \(A\) represents the nominal cross-sectional area of the specimens.
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 6 Performance Evaluation
### Comparison with Other Rain Rate Estimation Models: Brief Description
#### 6.1.1 The NEFOCAST System  
NEFOCAST [9] is the name of a project whose purpose is to design a system to detect and monitor rainfall by making opportunistic use of a network of ground terminals: as a matter of fact, such receivers intrinsically have the capability of measuring rain attenuation that affect the satellite downlink, starting from continuous measurements of the received signal-to-noise ratio (SNR). Each terminal sends the data to a satellite hub on the return link and such data are then redirected to the NEFOCAST service center, where the rain field maps are generated.  
The quantities defined below are expressed as a function of time \(t\). The steps that are required to retrieve the instantaneous rainfall intensity \(R(t)\) are the following:  
* obtain the reference SNR level in dry (no-rain) conditions, \(SNR_{dry}(t)\);
* measure the received signal-to-noise ratio \(SNR_{wet}(t)\) and determine whether or not rain is present along the link;
* in the presence of rain, extract the corresponding rain attenuation \(A_{R}(t)\), that is defined as the decrease in the measured SNR level with respect to \(SNR_{dry}(t)\); and,
* estimate \(R(t)\) from \(A_{R}(t)\) as: \[R(t)=a\left(\frac{A_{R}(t)\sin\theta}{h_{R}(t)}\right)^{b}[\mathrm{mm/h}],\] (19) where \(a\) and \(b\) are empirical, non-linear regression coefficients (whose values are reported in [9]), \(\theta\) is the elevation angle of the Earth-satellite link, and \(h_{R}(t)\) is the equivalent rain height. The latter quantity is expressed as: \[h_{R}(t)=h_{0}(t)-0.4\ [\mathrm{km}],\] (20) where the instantaneous values of \(h_{0}(t)\) are retrieved from NWP products.
# Machine Learning and Bagging to Predict Midterm Electricity Consumption in Saudi Arabia
## 6 Conclusions  
Electricity consumption prediction is essential for electric power networks to run efficiently. An accurate and reliable estimation of electricity consumption is necessary to plan for future electricity production systems to meet the rising demand for electrical energy. Since Saudi Arabia is one of the top electricity consumers worldwide, this research proposed a prediction model for electricity consumption in Saudi Arabia.  
The proposed models were developed using a new dataset provided by the Saudi Electrical Company for investigating the effects of machine learning techniques on predicting electricity consumption. Also, this dataset was the result of personal obtention and was not used before in any research. Furthermore, the dataset used in this work contains the electricity consumption of all areas covered by the SEC in Saudi Arabia divided by service offices, 18 of which are included in this study.  
This study used standalone K* and bagging ensemble models to predict electricity consumption for different regions in the Kingdom of Saudi Arabia (KSA). The results show that the bagging ensemble using a K* model outperformed the results obtained using the standalone model and achieved 0.9383 CC, 0.1511 MAPE, 0.5333 RMSPE, 0.0158 MAE, and 0.0484 RMSE. The experimental results achieved in this work were compared with the results of a previous study with the same dataset, and results show that the K* model performed better than the ANN model in the previous study when compared with the standalone models and the bagging ensemble. Furthermore, a comparison between the work performed in this study and past electricity consumption studies shows that the proposed electricity consumption model adequately forecasts electricity consumption for eighteen locations throughout Saudi Arabia.  
For future work, feature selection techniques as well as more machine learning models can be investigated. In addition, since fuzzy logic has already proven itself in predicting electricity consumption, it will be investigated in predicting electricity consumption in Saudi Arabia.  
Conceptualization, supervision, methodology, and proofreading, D.A.M.; implementation, analyses, and original draft preparation, M.A.A.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The dataset used in this work is available on request from the authors. The authors would like to thank the Saudi Electrical Company (SEC) for providing the electricity consumption data. The authors declare no conflict of interest. The authors declare no conflict of interest.
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 6 Conclusive Observations
### Priority-Based Distribution  
In general, the distribution models generated a distribution schedule for all the regions. The generalised distribution plan is not rational in disaster scenarios as the number of affected people and level of impact varies from one region to another. Prioritisation of the disaster regions in the distribution plan is required to support the victims who have been severely affected. One of the practical challenges in prioritisation is how to consider all the disaster regions in the distribution plan and avoid bias towards the severely affected regions. It has been observed that the condition of the less affected disaster regions worsens if there has been no relief distribution for a longer period [2]. This highlights that, in practice, any distribution model must consider the wait-time of all the disaster regions. The proposed dynamic model includes multiple priorities in terms of disaster impact severity and wait-time of all the disaster regions at all time-periods while generating the distribution schedule. This inclusion of multiple priorities makes the distribution rationalised and practically very effective, which is also very crucial in disaster conditions.
## 2 Related Work
### Fixed-Domain Relation Extraction (FRE)  
Fixed-domain relation extraction, also known as traditional relation extraction, requires defining entity types and relationships between entities in advance. Once the types of entities and relations are determined, no new entities and relations are extracted. Traditional knowledge extraction systems use predefined extraction rules or annotated data to learn extractors, mainly based on feature engineering and statistical supervised machine learning methods [10; 11; 12; 13; 14; 15; 16; 17]. These methods need a lot of annotated data. Other work is based on unsupervised methods [18; 19] by clustering representative relationship words and using these relationship words as triggers to extract relationships. Generally, FRE adopts two kinds of methods: the pipelined method and the joint model.  
Pipelined method: The pipelined method transforms the task into two subtasks, named entity recognition (NER) [20] and relation classification (RC) [21]. [PERSON] et al. [22] proposed an alternative paradigm called Distant Supervision to train a text classifier with a large unlabeled corpus. [PERSON] et al. [23] used two independent encoders to learn different contextual representations of entities and relations, respectively, and fuse entity category information at the input layer of the relational model.  
Joint model: pipelined models are often considered inferior to joint models because breaking the relation extraction task into subtasks can lead to error propagation, so the relation extraction work based on the joint model is also gradually expanded. [PERSON] et al. [24] proposed a novel tagging scheme that can convert the joint extraction task into a sequence labeling problem. [PERSON] et al. [6] used a handshaking tagging scheme that aligns the boundary tokens of entity pairs under each relation type.  
However, it is difficult to cover all relational facts in a predefined way. Once the domain data are replaced, the task not only needs to rename the entity relation but also needs to manually define new extraction rules or re-label new training data. As such, these systems rely heavily on human intervention.
# Evaluation of Radio Communication Links of 4G Systems
## 2 Background
### Model of the Propagation
#### 2.1.3 Cost 231 Hata Model  
The model of Cost 231 Hata is a widely used model. This model is introduced by [PERSON] in 1980 [19]. This model has the following parameters:  
* Transmitter height (30 to 100) m.
* Frequency range (500 to 2000) MHz.
* Size of UE (1 to 10) m.
* Link distance (>20 km). The path loss equation of this model is given in the following equation [1; 5]: \[\text{Lu}=46.3+33.9\log(\text{F})-13.82\log(\text{h}_{\text{b}})-\text{a}( \text{h}_{\text{m}})+(44.9-6.55\log(\text{h}_{\text{b}}))\log(\text{d})+\text{C}\] (8) \[\text{a(h}_{\text{m}})=1.1\log(\text{F})-0.7\times\text{h}_{\text{m}}-(1. 56\log(\text{F})-0.8);\] for suburban and rural environment \[\text{a(h}_{\text{m}})=3.2(\log(11.75\times\text{h}_{\text{m}}))^{2}-4.97;\] for urban environment  
where C is the correction factor set to 3 dB for urban locations and 0 dB for rural or suburban locations.  
F: Designates the frequency (MHz). \[\text{h}_{\text{b}}\] : Refers to the height of the base station (m). \[\text{d}\] : Refers to the distance (km). \[\text{h}_{\text{m}}\] : Designates the height of the mobile terminal (m). \[\text{a(h}_{\text{m}})\] : Correction factor for the height of receiver antenna.
# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 4 Research Methodology
### Feature Extraction  
This section provides a detailed explanation of the user, content, and emotion features that were used in our framework to detect non-credible tweets. _Content and User-Based Features_: Content and user-based features are crucial for detecting a non-credible tweet. A total of 47 features were extracted from each tweet using the Twitter API and divided into two categories: content-based and user-based features. Table 3 lists the user features in our dataset [39; 65; 66]. Twitter API documents contain a detailed explanation of each feature. The content-based features include the number of retweets, replies, favorites, presence of hashtags and URLs, the existence of photos, tweet length, and use of punctuation marks. These features are important indicators in social media of rumor verification, as non-credible tweets tend to have lower engagement rates and lack supporting evidence. For example, the presence of hashtags (e.g., #fakenews) was based on the observation of real posts that tended to contain more hashtags [50]. Furthermore, the presence of URLs in a post has been investigated in prior studies [67; 68]. According to their findings, supporting posts are more likely to contain links, indicating that they refer to information that confirms their claim. Moreover, the existence of photos is crucial for determining news veracity since real news tends to have photos or videos that support its content. Furthermore, replies can deny false news by posting images from authorities as evidence [69]. The word count was also used since false rumors are typically more detailed and lengthier, whereas true news is often concise [70; 71]. On the other hand, user-based \begin{table}
\begin{tabular}{c c} \hline \hline
**Statistics** & **Count** \\ \hline Events\# & 403 \\ Rumor\# & 202 \\ Non-rumor\# & 201 \\ News Posts\# & 20,493 \\ Replies\# & 40,759 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset Statistics. Figure 2: The proposed emotion-based rumor detection model. features include features such as follower count, number of status updates, number of friends, number of likes, verification status, length of time a user has held an account, and whether the user has a URL or textual description on their profile. Some features were directly obtained from the Twitter API, while others were derived indirectly. For example, the time span was computed by subtracting the time when a tweet was posted from the time when the related user was registered. User profile features have been used in previous studies to determine rumor veracity, with the findings indicating that false rumor conversations were often initiated by users with fewer friends and lower status counts, and that non-verified accounts were more likely to share false news [72; 73; 74].
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 2 Related Work
### Improvement of Energy Harvesting  
The efficiency of energy harvesting has been discussed in numerous studies [5; 7; 15; 18; 19; 20; 21; 22; 23; 24; 25]. Existing research includes hardware implementations [18; 19] and software [5; 7; 15; 20; 21; 22; 23; 24; 25] implementations.  
For hardware implementation, [PERSON] et al. proposed a new approach to receive more energy in wide-angle convergence by using a hybrid power combining rectenna array [18]. [PERSON] et al. showed that the shortcoming of average output DC power in ambient RF energy harvesting is nonlinearly dependent on antenna directivity and linearly dependent on the antenna port number [19]. Accordingly, they designed a directional 4-port pixel patch rectenna system to maximize average output DC power. For the directional energy transmission, [PERSON] et al. aimed to decrease the energy consumption of IoT nodes [5].  
In addition to proposing a node scheduling method to increase sleep nodes, an adaptive RF energy management method is proposed to automatically switch between data and energy modes, where the transmitter can monitor and control the voltage to adjust the charging intensity. [PERSON] et al. proposed an observation-based directional energy transmission algorithm for dynamically distributed IoT nodes [7]. The method can measure the high energy-efficiency sector and transmit RF energy to the selected sector. It is divided into two stages, namely sensing and transmission. In order to improve efficiency of energy harvesting, the probability of a high-energy-efficiency sector is increased by the detection stage. [PERSON] et al. proposed a phase-aware algorithm for the directional energy transmission based on multiple transmitters [15]. The RF energy waves can be combined either constructively or destructively by phase difference. The proposed method maximizes harvested energy with low energy consumption and a packet discard rate. There are several metaheuristic algorithms used for energy harvesting. [PERSON] et al. utilized the DE algorithm to optimize the energy efficiency [20]. They established an object function related to the total number of transmitted bits and the total energy consumption of the system. [PERSON] et al. proposed the artificial fish swarm algorithm to identify the optimal scheduling under the constraint of transmission energy [22].  
Recently, there have been several algorithms based on reinforcement learning. [PERSON] et al. proposed a reinforcement learning algorithm to learn the transmission rate, the current battery level of IoT devices, and predict the renewable energy harvesting model in order to determine the next decision [23]. [PERSON] et al. ensured that the AP can use the collected energy efficiently [24]. Their method uses deep Q learning to control transmit power, meet the data-rate requirement of users, and ensure devices harvest enough energy. [PERSON] et al. designed a two-layer Q-learning method to maximize the energy efficiency of AP and devices [25].
# Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces
## 3 Continuation of Existing in Services and Replacement Possibilities
### API-Based Service Invocation  
The API (Application Programming Interface) is a way for two (or more) computer programs to communicate with each other. The API can be considered also as a type of SW interface, offering service(s) to other pieces of SW. As overviewed in the next section, APIs have been already used widespread for a long time in telecommunication networks. First, APIs have been mainly used for internal CSP purposes, for example, to integrate customer relationship management into charging procedures. Integrating the Information Technology platform resources to the telecommunication (simply 'Telco') platform of the CSP has become another typical use case. The use of APIs has improved operational efficiency and reduced OpEx (Operational Expenditure) [17].  
Figure 5: Traditional IN-based services executed by the IM-SSF.  
APIs in an IMS AS may be also a valid alternative for IN replacement. In this approach, a standalone IMS AS or an MMTEL AS may expose network functions to API-based services, which again can decouple service logic from the IMS, enabling a shorter time to market. Up to now, the fusion of IT and Telco domains has been very challenging because they required different technologies and different platforms, and traditionally they had a different developer base. If Telco networks start using APIs instead of legacy Telco signaling-based services, then a similar platform can host both Telco and IT services [17, 50]. To conclude, APIs can be used not only for IN replacement, but APIs also enable fusion of services in the Telco and IT domains.
## 3 Potential of Earth Observation (EO) for Supporting SDG Indicators
Some applications require data over relatively short periods such as monitoring oil spills [95], forest fires [96], and sea ice motion [97], while, for understanding long term trends of environmental and climatological issues, long time series of images, and multi-decadal stability of the data set, which implies radiometric accuracy, are essential [97]. Other applications are in between, needing seasonal imaging especially for crop identification [98], phenology [99], and wetland monitoring [100]. Table 4 illustrates a representative range of EO applications with strong relevance to various aspects of sustainable development, although it should be noted that this is by no means an exhaustive list. Several peer-reviewed articles and case studies published since 2017 have explored the contribution of satellite imagery towards the SDG indicators and targets [14; 15; 27; 35; 36; 90; 154; 155; 156; 91; 157; 158]. Some examples are provided in Table 5.
## 2 Proposal of Vehicle-Mounted Transient Electromagnetic Method
### Detection Method of Tunnel Defects  
In the detection of tunnel defects, advanced technologies have been applied, including the digital photography method, laser scanning method, and ground penetrating radar method, as illustrated in Figure 5. The digital photography method can distinguish the water leakage at the surface of tunnel linings, but the amount of water leakage cannot be distinguished [10]. The laser scanning method can measure the convergence deformation [11]. The surface crack and local defect of surface defects can be identified by both the digital photography method and the laser scanning method [11; 12]. The above detection methods applied in tunnel surface defects can reach 50 km/h, and some can even exceed 80 km/h under the condition of low accuracy. As for internal defects, the ground penetrating radar method has been widely applied [13; 14; 15]. However, the detection speed of a single line can only reach 5 km/h in the actual field detection, due to the very close distance between the coil and the surface of the tunnel lining structure [13; 16]. The ground penetrating radar method can detect the insufficient thickness of the lining or inverted arch, and the dry cavity behind the tunnel lining. However, this method cannot effectively detect the hidden and undiscovered water behind the tunnel lining and the cavity-filled water [13; 15; 16]. Meanwhile, the detection speed of this method is relatively low at present, and so cannot meet the requirement of the rapid maintenance of a high-speed railway tunnel.  
The surface defects tend to be concentrated on the surface of the lining structures and are easily detected. In contrast, the internal defects are usually located between the lining structure and the surrounding rock, which means that these internal defects are difficult to find. Therefore, the rapid detection of internal defects is a serious challenge for tunnel maintenance engineers.  
In view of the limitations and shortcomings of current tunnel defect detection methods, it is urgent to find a new and rapid detection method in order to realize the rapid detection of the hidden and undiscovered water behind the tunnel lining and the cavity-filled water, so that the existing detection method of tunnel structural internal defects can be well supplemented and improved. In this condition, the application of a transient electromagnetic  
Figure 5: Detection method of tunnel structural defects.  
method to the detection of internal defects is fully and profoundly proposed by referring to the rapid detection of aviation TEM, as shown in Figure 5.