esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
A satellite communication antenna based on a waveguide slot antenna was designed to realize dual-frequency circular-polarized radiation through an X-shaped slot, and the antenna array unit spacing was optimized to achieve a wider bandwidth and smaller size. The experimental results showed that the impedance bandwidth of \(-10\) dB included the L-band 1.59-1.84 Hz and S-band 2.48-2.55 GHz, and the axial ratio bandwidth of 3 dB roughly covered the frequency bands of the uploaded and downtransmitted signals; the maximum gain at 1.6 GHz is 4.1 dBi. This antenna can be widely used as a satellite navigation terminal because of its simple feed and compact structure.  
Conceptualization, Y.L. and L.H.; methodology, Y.L., L.H. and H.L.; validation, Y.L.; writing--original draft preparation, Y.L. and H.L.; writing--review and editing, Y.L. and C.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Hubei Provincial Natural Science Foundation of China, The fund number is 2024 AFB966, and the person in charge is [PERSON].  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author and the first author.  
We thank the editor and the anonymous reviewers for their constructive comments that helped to improve our work.  
The authors declare no conflicts of interest. The funders 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.
\begin{table}
\begin{tabular}{l l} \hline \hline
**Descriptions** & **Abbreviations** \\ \hline Underwater Wireless Sensor Networks & UWSNs \\ IoT-enabled Underwater Wireless Sensor Networks & IoT-UWSNs \\ Energy Consumption & EC \\ Forward Layered Multi-path Power Control One & FLMPC-One \\ Forward Layered Multi-path Power Control Two & FLMPC-Two \\ Layered Multi-path Routing Control protocol & LMTC \\ Shortest Path First & SPF \\ Breadth First Search & BFS \\ Packet Received Ratio & PRR \\ Bit Error Rate & BER \\ Required Packet Error Rate & RPER \\ Noise Resources & NR \\ Noise Power & NP \\ Signal to Noise Ratio & SNR \\ Packet Error Rate & PER \\ Packet Delivery Rate & PDR \\ Distance from \(n-1\) to \(n_{th}\) node & D \\ Transmission Power & TP \\ Total Number of Data Packets & N \\ Total Hop Counts & THCs \\ Sparsity-aware Energy-Efficient Clustering & SEEC \\ Circular Sparsity-aware Energy-Efficient Clustering & CSEEC \\ Circular Depth-based Sparsity-aware Energy-Efficient Clustering & CDSEEC \\ Reliable Energy-Efficient Pressure-Based Routing & RE-PRR \\ Spherical Hole Repair Technique & SHORT \\ Geographic Forwarding based on Geo-spatial Division & GFGD \\ Greedy Geographic Forwarding based on Geo-spatial Division & GGFGD \\ Geographic and Opportunistic Routing with Depth Adjustment & GEDAR \\ Geo-Opportunistic Routing & GOR \\ Energy-Efficient Multi-hopping Routing & EAMR \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Abbreviations. \begin{table}
\begin{tabular}{l l} \hline \hline
**Definitions** & **Symbols** \\ \hline Absorption Coefficient & \(\alpha(F)\) \\ Binary Entropy Function & \(H_{2}\) \\ Transmission Energy & \((E_{t})\) \\ Receiving Energy & \((E_{r})\) \\ Reception Power & \(P_{r}\) \\ Total Energy & \(E_{Total}\) \\ Vertices & \(V\) \\ Channel capacity & C \\ Bandwidth & B \\ Path & P \\ Frequency & F \\ Packet Length & PL \\ Packet Size & PS \\ Data Rate & DR \\ Initial Energy of Network & \(E_{Initial}\) \\ Packet Type & \(P(t)\) \\ Total Time of Transmission & \(I_{hot}\) \\ Transmission Power & \(P_{I}\) \\ Data Packet & \(DP\) \\ Total Leftover Energy of the Network Nodes after Forwarding the Data Packets & \(E_{t}^{remaining}\) \\ Total Receiving Energy of the Network Nodes after Receiving the Data Packets & \(E_{r}^{remaining}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Acronyms.
# IECI: A Pipeline Framework for Iterative Event Causal Identification with Dynamic Inference Chains
## 4 Experiments
### Experimental Results and Discussion  
#### 4.3.1 Experiments for PRED  
To validate the effectiveness of PRED for event detection, comparative experiments are conducted on the EventStoryLine dataset and the MAVEN-ERE dataset. The comparison includes several state-of-the-art named entity recognition methods based on pre-trained language models, BERT-NER [38], BERT-CRF-NER [39], which integrates Conditional Random Fields (CRFs), and BERT-BiLSTM-NER [40], which combines BiLSTM with BERT.  
From the results of the EventStoryLine and MAVEN-ERE datasets in Tables 1 and 2, \"Runtime (min)\" refers to the time required to complete training and evaluation in minutes. The traditional BERT-NER method performs moderately in ED, with F1 scores of 85.6% and 86.1%, respectively. The BERT-CRF-NER model, with the introduction of Conditional Random Fields (CRF), shows a significant improvement in accuracy, reaching an F1 score of 88.2% and 89.3%, respectively. This demonstrates the advantages of CRF in dealing with label dependencies and sequence consistency. Further improvement is observed in the BERT-BiLSTM-NER model, which incorporates a sequence modeling mechanism. It achieves an F1 score of 91.7% and 92.2%, respectively, indicating that BiLSTM effectively enhances the model's ability to capture contextual information.  
Compared to the above methods, the proposed PRED module achieves the best performance, with a precision of 95.9% and a recall of 95.2% on the EventStoryLine dataset, while achieving a precision of 96.5% and a recall of 96.2% on the MAVEN-ERE dataset. The F1 score improves by 3.7% and 4.2% over BERT-BiLSTM-NER on the EventStoryLine and MAVEN-ERE datasets, respectively, and by 9.8% and 10.3% over BERT-NER. This mainly results from the introduction of a BiLSTM-based semantic role attention mechanism and a prompt-guided strategy, which enhance the model's capacity for event detection and its adaptability to complex event expressions. In addition, PRED maintains a high recall ability, verifying its ability to capture the full picture of events in their entirety. This provides more accurate and structured event inputs for subsequent causal inference tasks.  
Compared to the traditional BERT-NER family of models, PRED shows an increase in runtime. However, this extra cost results in better event recognition performance, suggesting an effective trade-off between accuracy and efficiency.
# Maximally Deficiency: Treatments and Applications for Adolescents
## 4 Comprehensive Clinical Consideration  
Based on the summary shown in Table 1, for children in primary and early mixed dentition, the appliances with the SME protocol, such as the quadhelix and removable expansion plate, provide gentle force, enabling effective expansion, but these appliances primarily induce dental rather than skeletal changes. For preadolescents in late mixed dentition and early adolescents, tooth-borne and tooth-tissue-borne expanders, including the HYRAX and Haas expanders, achieve greater skeletal expansion through sutural separation. Despite their efficacy, concerns such as significant relapse remain when using the RME protocol. For middle to late adolescents, bone-borne expanders and tooth-bone-borne expanders like the MSE, C-expander, ATOZ expander, and Hybrid HYRAX generate superior skeletal expansion with fewer dentoalveolar effects.  
As summarized in Table 2, regarding skeletally mature adolescents, conventional SARME is a straightforward and efficient procedure with minimal long-term impact on periodontal tissues, though it presents temporary aesthetic concerns and limited palatal bone expansion. Modifications such as cortical osteotomy, pterygomaxillary disjunction, and palatal preservation aim to enhance efficiency, respiratory function, and surgical invasiveness, but they are associated with increased complications, resistance concerns, and a lack of strong evidence. Segmental Le Fort I osteotomy is particularly beneficial for asymmetric expansions and dental alignment while minimizing midline disruption, though it carries risks of relapse and surgical complexity. For adolescents with congenital conditions or complex treatment needs, palatal segmental osteotomy provides precise control over dentoalveolar segment movement but demands advanced surgical execution. Distraction osteogenesis, using extraoral or internal distractors, enables substantial skeletal movement for severe deficiencies, with internal distractors offering better aesthetics and anchorage. However, external distractors pose social and scarring concerns, and both approaches require extended treatment durations. Osteotomy-assisted distraction and TSDO are viable for significant transverse expansion in severe maxillary deficiencies, but their stability, invasiveness, and effectiveness in non-growing patients remain key challenges.
## 3 Control Layer Architecture
### Directive Generation  
The function \(createDirective(m_{t+n})\) (_line 17_, Algorithm 2) is called to generate a directive encapsulating the context indicator prediction (\(m_{t+n}\)). The former function, defined in Algorithm 3, calculates the manipulated variable's value (\(\mu^{\prime}\)) from of the context indicator prediction and the reference value (_line 4_). Next, \(\mu^{\prime}\) is encapsulated in a directive: \(\delta_{s}=(\hat{H}_{s},\mu^{\prime})\) (_line 6_).  
As previously mentioned, the reception of a context measurement after contacting a node triggers the generation of a directive. Nevertheless, if the time window for receiving context measurements is set to a high value, it would take a controller a long time to generate a directive. Hence, the nodes would not receive any directive to adjust their initial configured manipulated variable (\(\mu\)) based on the current network condition, and they would have the perception that there is no controller nearby. Therefore, to prevent this situation, the controller is configured to generate a directive periodically, provided no directive has been generated opportunistically during this period. This directive encapsulates the last calculated manipulated variable (\(\mu\)), and it acts as a beacon announcing the presence of a nearby controller. Algorithm 3 describes the above behaviour.  
```
1\(\triangleright\)\(m_{t+n}\): Predicted measurement.
2\(\triangleright\)\(opp\): Execution mode's flag.
3\(\triangleright\)\(\mu\): Manipulated variable's current value.
4\(\triangleright\)\(\mu^{\prime}\): Manipulated variable's new value.
5\(\triangleright\)\(\tau\) Controller system's reference input.
6
7:functioncreateDirective(\(m_{t+n},opp=FALSE\))
8:\(\mu^{\prime}=\mu\)
9:if\(opp==false\)then
10:\(\mu^{\prime}\leftarrow\) apply controller_adjustment(\(\tau,m_{t+n}\))
11:endif
12:return new Directive(\(\mu^{\prime}\))
13:endfunction
```  
**Algorithm 3** Directive creation algorithm.  
Notice that the function _createDirective_ in Algorithm 3, receives the parameter \(opp\) which indicates whether the function is executed periodically, as described above or opportunistically after receiving a context measurement from a contacted node (see Algorithm 2). In the case of a periodic execution of the function _createDirective_, the new value for the manipulated variable (\(\mu^{\prime}\)) is directly the current one (\(\mu\)) (_lines 2_ and 6 in Algorithm 3). In the case of an opportunistic execution (_line 3_), the controller calculates the new value for the manipulated variable (_line 4_).
## 3 Case Study  
### System Initialization  
The benefits of the Q-learning algorithm for an intelligent home user and its ability to utilize surplus renewable energy were examined statistically using a community of six users and a power pool. The smart home users and the power pool exchanged energy every 12 h. The technique was found to be accurate at 12 h and 24 h. The suggested control approach was tested and found to meet the load demands in MATLAB/Simulink. The system simulated solar photovoltaic data (radiance and temperature) using Tunisian weather forecasts (Figure 5e,f). Regional weather data were updated every two days. The simulation results evaluated this strategy. The off-grid supply was one-way. Communal photovoltaic solar power was sufficient. Solar energy and the grid charged the batteries of each residential community with electricity. The optimizer used the batteries to reduce electricity prices during peak hours (Figure 5d). Table 2 shows the system parameters. Solar PV and the grid recharged the batteries during peak hours, producing bidirectional power from the grid. The solar PV responded to the load, while the batteries sold electricity to the grid (a house)--solar energy and grid energy charged off-grid batteries. Figure 5a,c show the PDFs fitted to the data with red lines. These distributions used 100 standard basis estimators evenly spaced between the two random variables. Figure 2a shows the two measures of network outage incidence from [49]. Figure 2b shows that the clusters had the lowest Davies-Bouldin values, but were too far apart. It also shows the typical network outages associated with the proposed strategy. The request for a non-reportable device comes from Ref. [50], which present exact statistics of an intelligent home over one year. Instead of modeling system losses, the raw data provide them. Load averaging converted the data to a 12 h resolution. The Tunisian grid locations provided solar irradiation and ambient temperature in this scenario.
## 3 RS Applications in Ocean
### Ocean Wave Height (OWH)
#### 3.3.6 Summary and Future Direction  
Most of the OWH studies using RS datasets have focused on data or algorithm-driven solutions, but the quality assessment of airborne/spaceborne-derived OWH under a variety of conditions has been investigated less. Moreover, application of advanced ML algorithms in analyzing, interpreting, and modeling RS data has received less attention from researchers, even though they can offer a great potential for accurate OWH mapping and monitoring tasks. For example, DL algorithms have rarely been used to model and estimate OWH from RS data, while they have significantly outperformed traditional methods by relying on their ability to train models with massive datasets. Indeed, DL methods can significantly facilitate understanding the complex structure of large RS data for OWH estimation/prediction.
## 2 Experiment Setups
### Data Processing  
The measured wind pressure at each tap can be expressed as a non-dimensional pressure coefficient \(C_{p}\) in the form of:  
\[C_{p}=\frac{P_{i}-P_{\infty}}{\frac{1}{2}\rho V^{2}} \tag{1}\]  
where \(P_{i}\) is the measured wind pressure, \(P_{\infty}\) is the static pressure at the reference point, \(V\) represents the incoming wind velocity equal to 11.2 m/s herein, and \(\rho\) represents the air density with a value of 1.25 kg/m\({}^{3}\).  
Due to the biaxial symmetry in the \(x\) and \(y\) directions (see Figure 3), only the wind loads in the \(x\)-direction are presented herein. The resultant wind force per unit height in the \(x\)-direction for the \(k\)-\(th\) pressure tap layer is denoted as \(F_{x,k}\), which can be obtained by:  
\[F_{x,k}=\sum_{i=1}^{N}\frac{1}{2}\rho V^{2}C_{pi,x}l_{i} \tag{2}\]  
where \(N\) is the total number of pressure taps in the \(k\)-\(th\) layer, \(i\) is the pressure tap number, \(C_{pi,x}\) is the \(x\)-component of the pressure coefficient for the \(i\)-th pressure tap \(C_{pi}\), and \(l_{i}\) is the control length of the pressure tap \(i\).  
Figure 3: Layout of pressure measuring points, test wind incidence angles, and coordinates of the measured wind force.  
The non-dimensional resultant layer force coefficient in the \(x\)-direction \(\mu_{x}\) can thus be determined by:  
\[\mu_{x}=\frac{F_{x}}{\frac{1}{2}\rho V^{2}B} \tag{3}\]  
As shown in Figure 4, the resultant layer force coefficient \(\mu_{x}\) can also be expressed as the superposition of the windward component \(\mu_{x,w}\) and the leeward component \(\mu_{x,l}\).  
The base shear coefficient \(C_{Ex}\) and the base bending coefficient \(C_{My}\) corresponding to the \(x\)-direction (see Figure 4), can be estimated by integrating the layer forces, thus:  
\[C_{Ex}=\frac{\sum\limits_{j=1}^{M}F_{x,j}h_{j}}{\frac{1}{2}\rho V^{2}BH} \tag{4}\]  
\[C_{My}=\frac{\sum\limits_{j=1}^{M}F_{x,j}h_{j}H_{j}}{\frac{1}{2}\rho V^{2}BH^{2}} \tag{5}\]  
where \(M\) is the number of the pressure tap layers, \(h_{j}\) is the vertical control width of the \(j\)-th tap layer, and \(H_{j}\) is the elevation height of the \(j\)-th tap layer.  
3 Mean Wind Pressure Coefficient at a Typical Wind Incidence Angle (\(\theta\) = 0 degand 45 deg)
# Economic Superiority of PIP Slip Joint Compared to Conventional Tubular Joints
## 2 Methodology
### Annual Expenditure Comparison  
Annual expenditure comparison refers to the analysis of how much is spent each year across different options--in this case, the three joint types: grouted joint, bolted flange joint, and PIP slip joint (Figure 3). It allows stakeholders to evaluate operational efficiency, cost-effectiveness, and long-term financial impact by comparing year-by-year expenses such as maintenance, inspection, and minor repairs. For this study, the expenditure comparison  
Figure 3: Average financial performance metrics.  
is shown in Figure 4. The graph shows that all three joint types incur high initial capital expenditures in the first year, representing fabrication and installation costs, with the grouted joint having the highest cost, followed by the bolted flange joint, while the PIP slip joint has the lowest initial investment--making it the most cost-effective option at the start. From year 2 onward, the expenditures stabilize across all joint types, reflecting consistent and relatively low annual operation and maintenance costs (approximately EUR 380,687). While the differences are minor, the PIP slip joint consistently exhibits the lowest ongoing costs, indicating greater financial efficiency throughout the project's lifetime. The graph illustrates the expenditure trends over a 21-year period for three joint types--grouted joints, bolted flange joints, and PIP slip joints. The initial capital expenditures are highest in year 0, with the grouted joint costing EUR 12.88 million, the bolted flange joint EUR 12.82 million, and the PIP slip joint costing the lowest at EUR 12.08 million. From year 1, expenditures drop sharply by over 96% to EUR 380,687.40 for all joints, representing ongoing operation and maintenance costs. This amount remains stable through to year 21, highlighting consistent annual spending. On average, the PIP slip joint demonstrates the lowest mean annual expenditure (EUR 937,893.23), followed by the bolted flange joint (EUR 973,056.58) and the grouted joint (EUR 975,712.17), reinforcing the PIP slip joint's cost-efficiency across the project lifespan.
# SC-SA: Byte-Oriented Lightweight Stream Ciphers Based on S-Box Substitution
### Problem Description  
Symmetric cryptography, as the most widely applied and researched cryptographic scheme in everyday life, plays a crucial role in safeguarding sensitive information during transmission and processing to prevent privacy breaches; however, existing symmetric cryptographic algorithms face several challenges, including high computational complexity, resource consumption, and encryption/decryption latency. These issues stem from the following design structures:  
* Cryptographic algorithms often involve intricate multiplication operations that are challenging to implement;
* The encryption process in cryptographic algorithms typically necessitates multiple iterations or rounds.  
As a result, traditional cryptographic algorithms are ill-suited for deployment on microdevices and resource-constrained IoT devices. Consequently, lightweight cryptography specifically tailored for resource-constrained and low-computing-power devices has emerged. Its primary objective is to reduce the complexity of cryptographic algorithms and minimize encryption/decryption latency and energy consumption in cryptographic deployments. In situations with limited computing power, cryptographic algorithms with higher computational complexity invariably lead to longer encryption latency, resulting in increased time for data processing and transmission.  
AES (Advanced Encryption Standard) serves as a typical traditional cryptographic algorithm that employs iterative round functions as design components. Subkeys, generated using the key K as the seed, participate in the iterations of the encryption/decryption process. While such symmetric algorithms exhibit high computational complexity, they provide the required strong security capabilities. In response to the call by NIST for lightweight cryptographic designs and the demand for lightweight cryptography deployments on microdevices and resource-constrained IoT devices, various cryptographic algorithms from different research fields have been proposed and adopted.  
To reduce resource consumption, most of these lightweight algorithms utilize static cryptographic components, emphasizing the minimization of cryptographic iterations while ensuring security. The use of dynamic design logic can expedite the reduction in the number of iterations; however, this introduces variable encryption components (units/logic). In such cases, both software and hardware implementations may necessitate more device resources.
## 2 Data
### Modis Ci  
The MODIS CI dataset was inverted from the MODIS bidirectional reflectance distribution function (BRDF) by the adjusted kernel-driven BRDF model [47; 48]. The CI has spatiotemporal differences that are greater in herbaceous vegetation such as grass, shrub, crop, and savanna than in forests [42] and shows strong seasonal changes, particularly in deciduous needleleaf forests [43]. The Savitzky-Golay logistic [49] method was utilized to eliminate the CI uncertainties resulting from clouds, fog, snow, ice, atmospheric dust, etc. The measurement extent of the eddy covariance flux towers is approximately 20-100 times the tower height, ranging from tens of meters to tens of kilometers [39; 50]. Therefore, the flux sites in CRO, GRA, SWA, OSH, and SAV with 10 m of vegetation should adopt the LAI of a cell (2 \(\times\) 2) with a 1 \(\times\) 1 km area, and the flux sites in ENF, DBF, and MF with over 10 m of vegetation should use the average LAI of cells (6 \(\times\) 6) with a 3 \(\times\) 3 km area.  
Additionally, the estimation of the CI of different seasons for every vegetation type is given by the synthesis inference combining the MODIS CI data and some published literature on the CI [32; 42; 43; 51; 52], which was used for optimizing the only constant \(\Omega\) parameter of the TL-LUE model.