esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Routing Protocols for UWSN
### Protocols Facturing Node Mobility
#### 3.1.4 Virtual Tunneling Protocol (VTP)  
With an aspect of improving the energy consumption, Packet Delivery Rate (PDR), transmitting path and latency, [PERSON] and [PERSON] proposed Virtual Tunneling Protocol (VTP) for UWSN [53]. There are three main steps in VTP: (i) choice of nodes for relay, (ii) tunneling to the destination from source, and (iii) packet transmission through tunnel.  
It is a cluster based protocol where all the nodes arrange themselves in several groups. Sinks are floated on the surface of the water which acts as the destination. All the clusters in the way of source to the destination is taken into consideration. Each cluster sends data from one to another towards the destination until the packet reaches any of the sinks. Two border nodes from each cluster are selected as relay nodes- one for receiving the data and one for sending it. Border nodes and the nodes which have transmitted the latest data packet are given the highest priority for being the relay nodes. In this way, the selected groups with their relay nodes form a virtual tunnel from source to the destination with good link quality. The collected data from the source is sent continuously to the destination through this tunnel. The transmission follows three-way handshake like TCP. Figure 6 explains the data transmission process of VTP in details.  
The figure shows that a virtual tunnel is formed from source to the destination. This tunnel transfer the data from one cluster to another depending on its distance to the sink. This follows three-way handshake between the relay nodes. Due to the thee-way handshake and tunnel formation, a strong link is formed from the source to the sink (destination). Authors showed that, VTP gives better result in Packet Delivery Rate, latency and number of hops in compared to the Channel Aware Routing Protocol (CARP) and multi-level Q-learning based routing protocol- MURAO [66, 67]. But this protocol do not show any results on energy-efficiency, reliability and overall performance. Moreover, VTP might not be energy conservative as it performs three-way handshake for single packet transmission.
# Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine
## 5 Methods
### 5.1 _Svm-Saa-Scr_
#### 5.1.3 Evaluation of SVM-SAA-SCR Performance  
The performance of the suggested SVM-SAA-SCR can be evaluated using several metrics, such as accuracy, precision, and recall. Here is one possible approach to evaluate the performance of the SVM-SAA-SCR:  
* Split the dataset into a training set and a test set.
* Train the SVM model on the training set.
* Use the trained model to predict the labels of the samples in the test set.
* Compare the predicted labels to the true labels of the test set samples.
* Compute the accuracy, precision, and recall of the model using the following formulas:
* Accuracy = (True Positives + True Negatives)/Total Samples
* Precision = True Positives/(True Positives + False Positives)
* Recall = True Positives/(True Positives + False Negatives)
* Interpret the results and make any necessary adjustments to the model or the data preprocessing steps.  
We note that accuracy is the percentage of correctly classified samples, precision is the percentage of correctly classified positive samples among all the positive samples, recall is the percentage of correctly classified positive samples among all the actual positive samples. The suggested SVM-SAA-SCR has the ability to classify sentiments using a small training set (PSAU dataset). It has also shown strong competition in achieving a high accuracy of 84.7%. The detailed performance results based on the PSAU dataset are detailed in Table 4 and Figure 8
## 4 Experimental Results
### Comparative Study for the Automatic Selection of the Number of Clusters
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **IND** & **PCQ** & **PCM** \\ \hline \multirow{3}{*}{Modularity} & SC & 0.28 & 0.29 & 0.30 \\  & TC & 0.67 & 0.34 & 0.42 \\  & Total Cost & 0.44 & 0.31 & 0.35 \\ \hline \multirow{3}{*}{Eigengap} & SC & 0.24 & 0.25 & 0.28 \\  & TC & 0.60 & 0.30 & 0.31 \\  & Total Cost & 0.38 & 0.27 & 0.29 \\ \hline \multirow{3}{*}{Density Peak} & SC & **0.11** & **0.08** & **0.10** \\  & TC & **0.25** & **0.10** & **0.12** \\ \cline{1-1}  & Total Cost & **0.17** & **0.09** & **0.11** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance under the density peak, the modularity and the eigengap algorithms. It is clear that the density peak algorithm gives the best results in minimizing the cost measures. For each time period, Figure 16 shows the variation of the total cost for the PCQ framework in the case of the density peaks, the modularity and the eigengap methods. We did not plot the results for PCM, which were similar to those of PCQ. It is shown that the density peak, the eigengap and the modularity methods partition the transportation network equally well. However, according to temporal smoothness, the density peak method should be preferred because it is more consistent with the recent history and gives the minimum total cost for PCQ and PCM. Figure 16: Variation of the total cost for the PCQ framework using the density peak, the modularity and the eigengap methods. The total cost function is defined as a linear combination of the snapshot cost (SC) and the temporal cost (TC). Figure 15: The number of clusters obtained by applying the density peak, the modularity and the eigengap methods for each time period.
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
## 2 Background Knowledge
### Elliptic Curve Digital Signature Algorithm  
_ECDSA_  
The high level of security of ECC relies on the difficulty level of the elliptic curve discrete logarithm problem (ECDLP). ECDSA depends on the elliptic curve cryptosystem. Like the other DSA, ECDSA has three stages. In the key-generation stage, a random number is multiplied by the base point of the selected curve to compute the public key. In the signature-generation stage, based on a secret key, a point multiplication and a hash function, additions are performed to find the signature. In the verification stage, the receiver recovers the signature from the received message and the public key, then compares it with the received signature. If the two signatures are identical, the received message is valid. Algorithm 1 explains ECDSA in detail.  
Figure 1: Digital signature algorithm.  
```
1:Key-generation
2:Prime \(p\), base point \(G\)
3:Public key \(Q\)
4:Select a random number \(d\) where \(1\leq d\leq n-1\)
5:Compute \(Q=d*G\) over the prime field \(F_{p}\)
6:Signature generation
7:Prime \(p\), base point \(G\), and the private key \(d\)
8:Signature \((r,s)\) for the message \(m\)
9:Select a random number \(k\) where \(1\leq k\leq p-1\)
10:Compute \((x_{1},y_{1})=k*G\) over the prime field \(F_{p}\)
11:Assign \(r=x_{1}\)
12:Compute \(e=Hash(m)\)
13:Compute \(s=k^{-1}*(e+d*r)\) over the prime field \(F_{p}\)
14:Verification
15:Prime \(p\), base point \(G\), signature \((r,s)\) of the input message \(m\), public key \(Q\)
16:The input message \(m\) is valid or invalid
17:Compute \(e=Hash(m)\)
18:Compute \(w=s^{-1}\) over the prime field \(F_{p}\)
19:Compute \(v_{1}=e*w\) over the prime field \(F_{p}\)
20:Compute \(v_{2}=r*w\) over the prime field \(F_{p}\)
21:Compute \((x_{1},y_{1})=v_{1}*G+v_{2}*Q\) over the prime field \(F_{p}\)
22:if\((x_{1},y_{1})=O\) where \(O\) is the point at infinity then
23:State message \(m\) is invalid
24:else
25: Assign \(v=x_{1}\)
26:endif
27:if\(v=r\)then
28:State message \(m\) is valid
29:else
30:State message \(m\) is invalid
31:endif
```  
**Algorithm 1** Elliptic curve digital signature algorithm (ECDSA).
## 4 Use Case in the Energy Wholesale Market
### Operational and Financial Impact of Worst-Case Scenario  
The decision to purchase additional energy at a higher cost, based on a worst-case scenario approach, carries significant operational and financial implications. From an operational standpoint, over-purchasing ensures a balanced load portfolio, which enhances the entity's stability even in the most challenging conditions. This strategy is designed to prevent supply shortfalls with minimal additional effort, thereby safeguarding continuous operations. Financially, while this approach does increase costs, it is a calculated measure to avoid potentially severe penalties. By planning for the worst-case scenario with a straightforward strategy, the entity can maintain financial resilience and ensure ongoing  
Figure 4: A qualitative visual representation of the allocated mix of both scenarios.  
operational continuity despite adverse market conditions. Additionally, as highlighted in the work of [PERSON] [36], the efficiency of risk management strategies like hedging can be significantly improved by minimizing forecasting errors, such as MAPE, thereby further reducing exposure to market risks. Reduced estimation error is beneficial for both utility companies and energy consumers. For utility companies, a lower MAPE means better accuracy in predicting electric load, which enables more efficient aggregation in the energy market. This accuracy helps the company remain competitive, avoiding significant cash flow disruptions that can arise from unexpected price fluctuations. On the other hand, end users benefit from more stable and potentially lower billing prices as accurate load forecasts help reduce the costs associated with energy procurement and generation, which are closely linked to market prices. The investment costs also play a crucial role in the initial investments for business domains that are affected by uncertainty, especially in the energy sector, as seen in other studies that leverage stochastic optimization approaches [42]. Please note that for the needs of this study, no further diversification was researched because the focus was invested in the benefits of AutoML methodologies for their usage in the energy trading sector.
## 2 Materials and Methods
### Check Marker Placement  
As the markers were attached on the lower limbs during data collection after T-pose measurement, it is necessary to check if the marker placement would be shifted during data collection, especially for the wand marker L/RTHI and L/RTIB during CLS. It was observed that the wand markers used have a fixed base which stops the wand markers from getting knocked out of alignment. In addition, using 10 randomly selected subjects, we measured the distances of KNE-THI during the T-pose and last dynamic trials, and the distances of ANK-TBI during the T-pose and last dynamic trials to compare the distance changes between the T-pose and last trials. It was found that the mean change of distances of KNE-THI were approximately 0.10 (SD 3.37) (mm), the absolute mean changes 2.40 (SD 2.23) mm, relatively mean changes 0.13% (SD 1.92%), and relatively mean absolute 1.40% (SD 1.23%). For the distance of ANK-TBI, the mean change of distances between T-pose and last trials were approximately 0.53 (SD 2.79) (mm), the absolute mean changes 2.08 (SD 1.81) mm, relatively mean changes 0.21% (SD 1.74%), and relatively mean absolute 1.31% (SD 1.08%). The distance change is very close within minimum accepted error. In other words, the marker placement was not significantly shifted.
## 2 Methodology
### Sample Definition and Steps for the Data Collection  
This review addresses two concepts: the UHI effect, and the different types of cool surfaces applied. To achieve this, an extensive search was carried out that employed the keywords linked to both concepts, as well as the satellite materials directly connected to the research field. Therefore, the first stage of the data collection was performed using the field, \"Title/Abstract/Keyword\", through the following keywords and search strings (Figure 2): \"Heat island\" OR \"Reflect\" AND \"Cool surface\"\", \"Cool facade\", and \"Cool roof\" AND \"Cool pavement\", within the Web of Science Core Collection and Scopus databases. The asterisk is used at the end of keywords to broaden the research. As a result, 982 publications were found, 347 of which were excluded after the deduplicating and cleaning of the raw data. After reading the abstracts, another 121 publications were excluded because they were not aligned with the purpose of this research. Finally, the bibliometric study was performed with 514 publications.  
The next step was dedicated to the restriction of the data and the refinement of the sample on the basis of a conceptual approach. According to the SciMAT visualization results, it was possible to follow the internal links in the cluster networks of the concepts with the highest potentials for reviewing, which returned 63 records. This process of data collection is described in the flowchart above (Figure 2). The analysis of these publications was the basis of the literature review of the evolution of cool surfaces through the materials applied to mitigate the UHI effect.
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
###### Abstract  
Duty-cycled sensor networks provide a new perspective for improvement of energy efficiency and reliability assurance of multi-hop cooperative sensor networks. In this paper, we consider the energy-efficient cooperative node sleeping and clustering problems in cooperative sensor networks where clusters of relay nodes jointly transmit sensory data to the next hop. Our key idea for guaranteeing reliability is to exploit the on-demand number of cooperative nodes, facilitating the prediction of personalized end-to-end (ETE) reliability. Namely, a novel reliability-aware cooperative routing (RCR) scheme is proposed to select k-cooperative nodes at every hop (RCR-selection). After selecting k cooperative nodes at every hop, all of the non-cooperative nodes will go into sleep status. In order to solve the cooperative node clustering problem, we propose the RCR-based optimal relay assignment and cooperative data delivery (RCR-delivery) scheme to provide a low-communication-overhead data transmission and an optimal duty cycle for a given number of cooperative nodes when the network is dynamic, which enables part of cooperative nodes to switch into idle status for further energy saving. Through the extensive OPNET-based simulations, we show that the proposed scheme significantly outperforms the existing geographic routing schemes and beaconless geographic routings in wireless sensor networks with a highly dynamic wireless channel and controls energy consumption, while ETE reliability is effectively guaranteed.  
Keywords: beaconless geographic routing cooperative communication; duty cycle; energy efficiency; reliability-aware +
Footnote †: journal: Sensors
## 4 Results
### Support Vector Machine (SVM)  
This section briefly describes the outcomes of the SVM algorithm, emphasizing its purpose and impact on the implemented federated learning architecture.  
1. Global vs. Local Model Training: The SVM model's evaluation parameters provide outstanding performance on a number of measures, as Figure 8 illustrates. The model predicts with a high overall accuracy level of 95%. Besides, the model demonstrates its ability to provide accurate and trustworthy positive identifications with an accuracy rate of 94%. At the same time, a 96% recall rate implies that it recognizes selective forwarding attacks taken as prominent instances. The 95% F1 score, for false negatives and false positives, shows that generally the model has no problem balancing between precision and recall. Taken together, it is evident that SVM can detect patterns that indicate selective forwarding attacks.
2. Confusion Matrix of SVM: The confusion matrix provided in Figure 9 illustrates the high performance of the SVM model while applying federated learning for recognizing selective forwarding attacks in IoT networks with the RPL protocol. The program  
Figure 8: SVM Training Model.  
Figure 7: LR Confusion Matrix.  
displays exceptional accuracy in accurately recognizing scenarios that do not include selective forwarding attacks, totaling 13,627 occurrences. Furthermore, it is quite accurate in detecting the occurrence of these assaults, identifying 14,808 instances as selective forwarding behavior. The results demonstrate the model's exceptional sensitivity and specificity, as well as its ability to successfully strengthen IoT networks against hostile behavior. The ability to accurately distinguish between safe and malicious network data is critical for improving the security of RPL-enabled IoT setups. This provides a solid foundation for future optimization efforts to eliminate misidentifications and improve detection systems.
## 2 Materials and Methods
### Quantification of Volatiles  
The volatiles were analyzed using headspace solid-phase microextraction (HS-SPME) coupled with GC-MS as described by [PERSON] et al. [25]. A 50/30 \(\upmu\)m DVB/CAR/PDMS fiber (Supelco, Bellefonte, PA, USA) was used to extract the volatiles. In a 20 mL gas-tight vial, 2 g of NaCl, 2 mL of wine, 6 mL of pure water, and 20 \(\upmu\)L of internal standard (16 mg/L, 2-octanol) were added and incubated in a 40 \({}^{\circ}\)C water bath with stirring at 600 rpm for 15 min, extracted for 30 min, and desorbed in the GC injection port (230 \({}^{\circ}\)C) for 5 min using a Shimadzu QP2020 GC-MS (Shimadzu Corporation, Kyoto, Japan) and a DB-WAX column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; Agilent Corporation, Santa Clara, CA, USA). The carrier gas used was helium (99.999%) at a flow rate of 1.5 mL/min. The GC program was as follows: 40 \({}^{\circ}\)C for 3 min, raised to 160 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min, followed by an increase to 220 \({}^{\circ}\)C at 7\({}^{\circ}\)C/min, and hold for 8 min. MS transfer line and ion source temperatures were set to 220 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, respectively. Electron ionization (EI) mass spectrometric data from \(m/z\) 35 to 350 were scanned at 0.2 s intervals. Esters were identified by comparing their retention time and mass spectra with those of pure standards using the NIST 17 mass spectral library. Target compound concentration was calculated by interpolation of relative areas in the calibration graphs obtained with the pure reference compound (2-octanol).
## 3 Results
### Heavy Rain Disaster Risk Assessment
#### 3.2.6 Risk Assessment and Validation
Relatively high-risk and high-risk zones are concentrated in the south-central part of Beijing, southern HebeiProvince cities such as Shijiazhuang, Hengshui, and Xingtai, eastern Hebei cities like Chengde and Qinhuangdao, and southern Tianjin. The proportion of medium-risk zones to the total area remains relatively stable. In Zhangjiakou, located in the northwestern plateau area, is predominantly low-risk for heavy rain disasters, with small portions in the central and southern areas classified as medium-risk. The improved MAML-RF-SHAP model in this study demonstrated high simulation accuracy and reduced training time in assessing heavy rain disaster risk. To further verify the accuracy and reliability of the assessment results, this study conducted a comparative analysis using actual administrative district disaster data from the _China Meteorological Disaster Yearbook_ for four typical years. According to the yearbooks, in 2003, cities severely affected by heavy rain disasters included Tangshan, Qinhuangdao, Hengshui, and Xingtai in Hebei Province [50]; in 2005, severely affected areas included Miyun District in Beijing and urban areas of Tianjin [51]; in 2012, heavy rain in Tianjin resulted in 118,000 hectares of affected crops, 14,000 hectares of total crop loss [52]; in 2016, heavy rain led to flash floods in the Taihang Mountain area of western Shijiazhuang, Xingtai, and Handan in Hebei Province, as well as severe urban waterlogging [53]. Therefore, the MAML-RF-SHAP model  
Figure 8: Heavy rain disaster risk zoning grade map: (**a**) 2003; (**b**) 2005; (**c**) 2008; (**d**) 2016.  
proposed in this paper effectively identified relatively high-risk and high-risk areas in the heavy rain disaster risk assessments for the Beijing-Tianjin-Hebei region across four typical years with varying degrees of disaster loss, aligning closely with the administrative regions recorded in the corresponding year's meteorological disaster yearbooks.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 3 Sensor Node Design
### Communicating Unit  
Once the essential data processing is done, the information can be communicated to the access point [22]. Several standards have been proposed in the recent years for the IoT applications [11; 12]. Among these, Low-Power Long-Range radio (LoRa and LoRaWAN) are gaining a lot of interest because of the high receiver sensitivity (down to \(-137\) dBm). These technologies can reach long transmission distance (up to 20 km). In our design model, LoRa/LoRaWAN are implemented using the Semtech's _XX1272_ transceiver.
## 5 Adaptive Algorithm  
### Objective of Adaptivity  
The adaptive FE analysis in this paper is of the adaptive time-stepping (element size) type, i.e., the analysis is implemented element by element with each element size adaptively adjusted. Let \(e\) denote a representative element. The ultimate objective of the adaptive analysis in this paper is to find an optimal element size \(h\) for each element \(e\), such that the reduced solution \(u_{\rm R}^{h}\) of the reduced element of degree \(m\) obtained on the element \(e\) satisfies the user-specified tolerance \(T_{l}\) in the maximum norm:  
\[\max_{e}\left|u-u_{\rm R}^{h}\right|\leq T_{l}. \tag{19}\]  
Figure 2: An illustrative sample of one element solution (\(h=3.5\)). For the reduced linear solution \(u_{\rm R}^{h}\), the true error \(u-u_{\rm R}^{h}\) is very closely estimated by the calculated error \(u_{\rm Q}^{h}-u_{\rm R}^{h}\) with higher order nodal errors.  
Since the exact solution \(u\) is generally unknown, the full order solution \(u^{h}\) of degree \(m+1\) is used to replace \(u\) for error checking, and Equation (19) becomes  
\[\max_{\varepsilon}\left|u^{h}-u^{h}_{\mathrm{R}}\right|=\max_{ \varepsilon}\left|e^{h}_{\mathrm{R}}\right|\leq T_{l}. \tag{20}\]  
Note that the above maximum error can be directly calculated as shown in Equation (18). Letting \(\varepsilon^{h}_{\mathrm{max}}\) and \(\varepsilon^{h}_{\mathrm{max}}\) denote the estimated error ratio and true error ratio defined respectively by  
\[\varepsilon^{*h}_{\mathrm{max}}\equiv\max_{\varepsilon}\left|e^{h}_{\mathrm{ R}}\right|/T_{l},\;\varepsilon^{h}_{\mathrm{max}}\equiv\max_{\varepsilon} \left|u-u^{h}_{\mathrm{R}}\right|/T_{l}. \tag{21}\]  
By introducing a pair of bounds for the estimated error ratios, the stopping criterion in Equation (20) is replaced in practical implementation by the following criteria  
\[\varepsilon^{*h}_{\mathrm{max}}\in(\gamma_{l},\gamma_{u}). \tag{22}\]  
In this paper, we simply take \((\gamma_{l},\gamma_{u})=(0.1,0.85)\) throughout.