esa-sceva/satcom-chunk-collection huggingface dataset:
# A Four-Port Dual-Band Dual-Polarized Antenna for Ku-Band Satellite Communications
## 1 Introduction
Moreover, the antenna has four ports, making it easy to tune the two operating frequency bands at different ratios.
# A Numerical Study of the Behavior of Micropile Foundations under Cyclic Thermal Loading
## 3 Results and Discussion  
### Mechanical Behavior  
#### 3.1.1 Load-Displacement Response of an Isolated Pile  
The design of axially loaded micropiles broadly follows the same procedures as those for large diameter piles, see, e.g., [56] where the desired outcome is for a pile that operates safely and within acceptable movement limits. In this case, floating bored (Type A and B) micropiles are considered and only the resistance on the pile shaft is used to meet theotechnical design requirements. The base resistance is ignored because often it is a small  
Figure 3: (**a**) Seasonal surface temperature boundary conditions applied, and (**b**) development of initial temperature profile. component of the total resistance and there is always some uncertainty as to its reliability, due to the possibility of either soil disturbance or drilling debris remaining in the bottom of the pile bore. When used as a conventional load bearing element, allowable compression and tension loads, \(\mathrm{F_{a}}\), applied to a micropile in service will be derived from the ultimate shaft load resistance, \(\mathrm{R_{s,u}}\), divided by a suitable factor of safety on shaft resistance FS-shaft. For micropiles, the value for FS-shaft used in practice varies between 2.0 and 2.5, depending on the level of field testing to be undertaken to prove the resistance values [57]. A similar range of global factor of safety is arrived at depending on the proportion of variable loading when applying [58]. The micropile analyses undertaken in this study were, therefore, based on FS-shaft = 2, i.e.,  
\[\mathrm{F_{a,MP}}=\frac{\mathrm{R_{s,u}}}{2.0} \tag{3}\]  
where the ultimate shaft resistance, \(\mathrm{R_{s,u}}\) was defined as the value of the shaft resistance mobilized at a settlement equal to 25% of the pile diameter (D), during an analysis of the pile under mechanical compression load and using the largest domain radius (\(\mathrm{R/D}=30\)), which allows the pile to be considered as isolated, Figure 1. When used for settlement control, as envisaged by the original _pali radici_ method [10], the design factor of safety may be lower than two, but this has not been considered here, as the design of _pali radici_ depends on an analysis of strain compatibility between the existing foundation and the micropiles. Figure 4 illustrates the predicted load-displacement response of the micropiles, along with the results for a large diameter (LD) pile, as reported in [34]. Based on these analyses and with the value for \(\mathrm{R_{s,u}}\) taken at a pile settlement ratio, \(\mathrm{y/D}=25\%\), Equation (3) was used to define the allowable service load, \(\mathrm{F_{a}}\) (Table 2), used in the thermo-mechanical analyses that follow, for the micropile cases. Additionally indicated are the assessed ultimate resistance and the global factor of safety (FS-global = \(\mathrm{R_{c,u}/F_{a}}\)) which includes the contribution from the base resistance. The values associated with the LD pile given in the table were defined in [34].
# Detecting Deception from Gaze and Speech Using a Multimodal Attention LSTM-Based Framework
## 3 Results  
### Results with a Single Modality  
Table 1 contains the DACC, HACC, ACC and AUC at segment and turn level achieved by the SVM and LSTM-based systems with the two modalities under consideration: gaze and speech. Additionally, for a better visualization of the results, Figure 4a shows the mean and standard deviation of the AUC at segment level obtained by all the systems evaluated in this work, whereas Figures 4b contains the same information at turn level.  
Firstly, it can be observed that LSTM-based systems outperformed the reference SVM-based ones regardless of the input features for all metrics. The only exception was the performance of the speech modality in terms of deception accuracy for the LSTM system that was worse than for the reference one. However, in this case, the honesty accuracy was considerably better for the LSTM-based system. The differences in performance between SVM and LSTM-based systems were especially noticeable for the gaze modality. In fact, in terms of AUC, LSTM with the speech modality achieved a relative increase with respect to SVM and speech of 8.69% and 8.10% at segment and turn level, respectively, whereas with LSTM and gaze a relative increase was obtained with respect to the corresponding baseline of 26.39% and 33.77% at segment and turn level, respectively.  
Secondly, focusing on LSTM-based systems, it can be observed that at segment level, gaze features were more able to correctly detect deception than honesty. In contrast, the speech modality performed considerably better for detecting truths than lies. The performance at turn level presented the same trends.  
Finally, regarding the general comparison between the modalities in terms of AUC in the LSTM framework, gaze outperformed speech, especially at turn level, where this first modality achieved a 22.83% relative increase in AUC with respect to the second one.
# Performance Evaluation of Bluetooth Low Energy: A Systematic Review
## 2 BLE Functioning
### BLE Communication
#### 2.2.2 Connections
During this process, an Acknowledgment packet (ACK) is sent back to the _master_ in order to communicate to the _Host_ layer if and how many packets were correctly transmitted. This type of communication, with the ACK packet, is called _round-trip_. A similar process is shown in (**d**), where the _slave_ writes on its readable _characteristics_ and the _master_ reads the data. Also in this type of communication, there is the transmission of an ACK packet, so this is also a _round-trip_ communication. In (**e**), a _one-way_ communication is represented, where the _slave_ communicates with the _master_ using a notifiable _characteristic_, which means there is no ACK packet. the rest of the time, when the communication is off, is the _Radio ldle_. The start of a _comEvent_ is called the _anchor point_; at the _anchor point_, the _master_ shall start to transmit packets to the _slave_. Other parameters, useful to describe the BLE _connection_, can also be used to set up the _connection_ itself [59]:  
* _Connection Interval (comInterval)_ is the time between the beginning of two consecutive _connEvents_; in other words, it is the sum of _connEvent_ and _Radio ldle_. The _connInterval_ shall be a multiple of 1.25 ms in the range of 7.5 ms to 4.0 s.
* _Connection Supervision Timeout (connSupervisionTimeout)_ is the maximum time that can flow without receiving two valid packets, before the _connection_ is lost. The _connSupervisionTimeout_ should be a multiple of 10 in the range of 100 ms to 32,000 ms.
* 1). Moreover, _connSlaveLatency_ shall not be less than 500, and when it is set to zero, the _slave_ device shall listen at every _anchor point_, without loosing the _connection_. As can be seen in Figures 7 and 8, a data packet exchange is usually followed by another packet called Acknowledgment (ACK), i.e., a packet without data, which provides error recovery capabilities. Depending on the type of communication, i.e., when data are sent through a notifiable _characteristic_, the ACKs are not sent. There are two main communication modalities the _master_ can use to exchange data with the _slave_[50]:  
* In _one-way_ ATT communication, the _slave_ sends a simple notification in response to a poll from the _master_. This is typical of the communication through notifiable _characteristics_. * In _round-trip_ ATT communication, the _master_ firstly asks for data to the _slave_, then this one transmits a response. The difference is that both messages, the request and the response, generate an ACK. The interval of time between the beginning of two consecutive data packet, including the ACK, is called \(T_{round-trip}\).
# Latency-Aware Accelerator of SIMECK Lightweight Block Cipher
## 4 Implementation Results and Comparisons
### Comparisons  
For comparison to the state-of-the-art, we have considered SIMEK implementations running at maximum operating frequencies. Therefore, the comparison to existing designs is given in Table 3. The first column provides the reference designs. The utilized algorithm or implementation technique is given in column two. Column three presents the implementation device. Hardware resources in terms of LUTs and Slices are described in columns four and five, respectively. The clock cycles, operational frequency, and latency of the designs are given in columns six, seven, and eight, respectively. The last column (i.e, nine) presents the throughput of the reference designs. Finally, we used a special symbol (i.e., \(-\)) in Table 3 where the relevant information is not provided. The comparison to area and timing parameters is described in Section 4.3.1 and the throughput comparison is shown in Section 4.3.2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Algorithm/Technique** & **Device** & **LUTs** & **Slices** & **CCs** & **Fre (MHz)** & **Lat (\(\mu\)s)** & **Thr (Mbps)** \\ \hline \multirow{4}{*}{[20]} & Baseline & Artix-7 & 95 & 40 & 32 & 387.6 & 0.082 & 12.195 \\ \cline{2-9}  & two-stage pipeline & Artix-7 & 179 & 57 & 16 & 297.7 & 0.053 & 18.867 \\ \cline{2-9}  & folded & Artix-7 & 94 & 33 & 32 & 370.9 & 0.086 & 11.627 \\ \cline{2-9}  & DSP-targeted & Artix-7 & 117 & 43 & 32 & 349.8 & 0.091 & 10.980 \\ \hline \multirow{3}{*}{[30]} & SIMECK64/128 & Spartan-3 & 226 & 140 & 44 & 166.61 & 0.264 & 3.787 \\ \cline{2-9}  & SIMECK64/128 & Spartan-6 & 259 & 206 & 44 & 217.65 & 0.202 & 4.950 \\ \hline \multirow{3}{*}{[31]} & SIMECK64/128 & Artix-7 & 139 & 92 & 48 & 172.41 & 0.278 & 4.950 \\ \cline{2-9}  & SIMON32/64 & Spartan-6 & 328 & 113 & 44 & 50 & 0.880 & 1.136 \\ \hline \multirow{2}{*}{[26]} & KLEIN-64 & Spartan-6 & – & 105 & – & – & – & – \\ \hline \multirow{2}{*}{[27]} & QARMA-128 & Kintex-7 & – & 418 & – & 100 & – & – \\ \hline \multirow{2}{*}{[28]} & LED64/64 & Spartan-3 & 274 & 114 & 32 & 167.34 & 0.191 & 2.32 \\ \cline{2-9}  & & Kintex-7 & 157 & 49 & 16 & 382.25 & 0.041 & 24.390 \\ \cline{2-9}  & & Artix-7 & 164 & 50 & 16 & 388.52 & 0.041 & 24.390 \\ \cline{2-9}  & & Spartan-3 & 196 & 129 & 16 & 198.62 & 0.080 & 12.500 \\ \cline{2-9}  & & Spartan-6 & 204 & 195 & 16 & 268.92 & 0.059 & 16.949 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The state-of-the-art comparisons of block ciphers.
## 4 Experimental Evaluation
### Prospects for Real-World Conditions  
In real-world implementations, the RES contribution possesses the priority in the energy mix. Consequently, the penalty costs for the power not served from variable resources become superior in order to eliminate their curtailment. By enhancing the variable and uncertain resource penetration, the overall system inertia in terms of spinning reserve decreases, since the conventional generating units constitute the only source for the spinning reserve provision. An actual paradigm representing the residual load in contrast to the variable energy sources integration is depicted in Figure 10.  
The actual values of the considered energy system pertaining to the worst day are tabulated in Table 3, along with the dynamic, spinning reserve requirement.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Interval** & **Biomass** & **Geothermal** & **PV** & **Wind** & **Hydro** & **Residual Load** & **SR** \\ \hline
1 & 10 & 18 & 0 & 23 & 305 & 1458 & 45 \\
2 & 10 & 18 & 0 & 2 & 296 & 1436 & 37 \\
3 & 10 & 17 & 0 & 0 & 287 & 13  
The sharp changes in the net load observed during periods with a high RES contribution require rapid-response generators to get online and follow the curve with minimum deviations. Apart from the increased ramping capability, these generators are usually expensive to start up, and in power systems with frequent renewable intermittency (e.g., due to unexpected cloud occurrences or steep wind gusts or falls), they may start up/shut down multiple times a day. On the contrary, the fuels used by rapid units with a high ramp capacity contribute less in terms of GHG emissions than they do in terms of energy consumption. They offer cleaner conversion with poor combustion indices [38]. Table 4 lists the CO2 emission coefficients of the assessed generators in relation to their fuel type [29]. The indicated values account for 55/tn of released CO2 [37].  
It is worth noting that expert systems and efficient algorithms are needed to handle the exploration-exploitation rates and, avoiding the local optima trapping, guide the optimization task towards global UC schedules with minimum _TPC_ and maximum reliability. A realization of the problem based on the proposed approach is included in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Unit i** & **e\({}_{1}\) (5/MW\({}^{2}\)h)** & **e\({}_{2}\) (5/MWh)** & **e\({}_{3}\) (5/h)** & **SU Emission** & **Fuel Type** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 4: Emission features of the thermal generating units [39].  
Figure 11: Optimal UC schedule obtained by the proposed approach in a real-world scenario.
## 3 Results And Discussion
### One Versus All Results  
In a second stage, the authors performed a more exhaustive comparison to select the combination of classifier and features on which learning gives best results. The number of comparisons tested simultaneously increased along with likelihood of erroneous inferences, thus requiring a more strict significance threshold. Regarding precision, it was found that between multiple comparisons, there were differences with a \(p<0.000001\). This allowed us to further inspect pairwise relationships: in particular, the CNN classification has a mean rank greater than CNN extended weighted regression and CNN weighted regression with an equal \(p=0.001\). SVM on workflow II ranked in the middle, with performance being greater than CNN extended weighted regression with \(p=0.025\) and with respect to CNN weighted regression with \(p=0.018\).  
Friedman test on recall or sensitivity to positive cases does not reveal statistically different pairwise comparisons among classifiers and workflows with \(p=0.304\), but the three CNN implementations rank best. Statistical analysis regarding f1 score states the  
Figure 6: Workflow III. Boxplot of precision, recall, and f1 score distributions on test sets for weld defective class among three classifiers performing on ‘feature selection’ setup.  
dominance of the CNN with classification layers on CNN extended weighted regression with \(p\) = 0.025 and on CNN weighted regression with \(p\) = 0.013.  
The Friedman test does not reveal any statistically significant comparison among performance distribution about negative class, yielding \(p\) = 1.0 in each performed analysis.  
Summing up, CNN with classification output layer performed best in both precision and f1 score with respect to other implementations, learning from the pooled group of features (see the Table 1). In fact, it can be stated that CNN with classification layer and trained on handcrafted features performed best among classifiers across the workflows, both regarding precision and f1 score for positive class. Thus, the initial hypothesis is fulfilled. In addition, feature reduction workflow has shed light on the ability of the SVM architecture to benefit from suppression of an amount of irrelevant features in the data samples.
## 4 Discussion
These differences in behavior significantly impact the heat transfer process, enabling machine learning and deep learning models to effectively distinguish the heat transfer characteristics of different fluids based on these features. Although this study has achieved significant results, there are still certain limitations that require further improvement in future research. Firstly, the scale and diversity of the dataset are restricted. The dataset used in this study primarily originates from numerical simulation results under specific operating conditions, with a relatively limited number of samples and predominantly focused on specific channel shapes and sizes. In the future, the dataset can be expanded by incorporating data from diverse operating conditions, various channel configurations, and extreme scenarios to enhance the model's generalization capability. Secondly, the interpretability of the model requires further enhancement. While the SHAP method provides a degree of interpretability analysis, the understanding of the internal mechanisms of deep learning models remains limited. In the future, additional interpretability techniques, such as feature importance analysis and activation function visualization, can be integrated to further elucidate the internal workings of the model. Additionally, this study primarily focuses on predicting heat transfer coefficients but lacks in-depth exploration of other critical thermodynamic performance parameters, such as pressure drop and thermal efficiency. Future research can broaden its scope to predict and optimize these parameters for a comprehensive evaluation of PCHE performance. Therefore, future research will focus on the following four aspects: (1) Expanding the dataset to include multiple working fluids, varied channel configurations, and extreme operating conditions to improve the engineering versatility of the model; (2) Overcoming the interpretability limitations by integrating physics-informed neural networks (PINNs); (3) Investigating the combination of multimodal attention mechanisms and graph neural networks (GNN) to more accurately represent the coupling effects between channel topology and local flow characteristics; (4) Exploring alternative deep learning models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs) to further enhance prediction performance.
# Recent Progress in Fast Distributed Brillouin Optical Fiber Sensing
## 6 Conclusions  
Brillouin-based optical fiber sensing can offer good solutions for the modern transportation and industrial facilities thanks to its significant advantages. For the classical BOTDA scheme, the frequency-sweeping process to obtain the BGS is generally executed for several seconds or minutes, which limits its applications in static or slowly-varying measurements. In this paper, several improved BOTDA schemes proposed to break the speed limitations have been summarized: the OFC scheme, the OFA scheme, the SA scheme, and the OCC scheme. We believe that the combination of two or more schemes can provide a better distributed ultra-fast measurement solution for the capturing of the moving objects, high-temperature warning of buildings, and health monitoring of large infrastructures.  
Writing--Review & Editing, H.Z., D.Z., and Y.D. Resources, B.W., C.P., P.X., and T.J. Supervision, Y.D. Project Administration, Y.D., H.Z., H.L., and D.B.  
This work was supported by the National Key Scientific Instrument and Equipment Development Project of China (2017 YFF0108700), the National Natural Science Foundation of China (61575052), and the Natural Science Foundation of Heilongjiang Province, China (QC2015087). D.Z. acknowledges the financial support from the China Scholarship Council (CSC) program (201706120112).  
The authors declare no conflict of interest.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Aloha-Based Protocols
#### 5.1.5 Random Frequency Time Division Multiple Access (RFTDMA)  
Despite not being reported in satellite environments, this protocol has an important relevance in low power wide area (LPWA) technologies, more precisely for its use in SigFox. Considering the ultra narrowband (UNB) technology, this protocol acquires prominence when low cost transmitters that do not require expensive oscillators are required to perform a precise adjustment of the carrier frequency. Taking this into account, random frequency time division multiple access (RFTDMA) uses the time and frequency to send messages without discretization (as in pure Aloha) [38]. Figure 7 illustrates the process of a transmitter selecting a frequency according to the protocol, a representation of the channel resulting from many nodes transmitting at the same time and, finally, the receiver's architecture to retrieve the data out of the composite signal.  
The performance of this protocol is calculated in [56]. Its maximum normalized throughput is lower than 0.1 for \(C\simeq 0.25\), and the \(PLR\) results are not reported as a function of \(C\).
# A Systematic Review of the Usability of Telemedicine Interface Design for Older Adults
## 3 Results
### Factors Affecting the Usability of Interface Design
#### 3.2.3 Visual Design
Users prefer bar charts for clarity and accuracy in data feedback [77; 79], and it is suggested that emojs be used instead of charts, as they serve as a universal language for most users [77; 79].
## 3 Related Work
#### Related Work on TSP Optimization with Quantum Computers  
Unlike for the use of the QAOA for Max-Cut, very few attempts have been made to use the QAOA for TSPs with a large number of nodes, i.e., with \(n>4\). This is mainly due to the limited qubit size and scalable quantum simulators available. In 2018, [PERSON] et al. proposed a quantum algorithm to solve the TSP using the phase estimation technique. The authors encoded the distances between the cities as phases and applied the phase estimation algorithm to compute the total distances for all the possible routes [21]. Though a quadratic speedup over the classical brute force method for a large number of cities was claimed, the actual demonstration was performed only for a four-node TSP using an IBM quantum simulator.  
Some initial attempts to adapt quantum formulation to graph optimization using ACO were reported in [22; 23]. The solution of the TSP with time windows (TSPTW) in quantum computers was developed in [24]. It looks for a tour with the minimum cost where each city is visited within an associated time frame, namely, between its earliest start time and its due time. The authors investigated the implementation of this solution on a D-Wave computer with only up to five nodes. Recent work [25] formulated the TSP as Quadratic Unconstrained Binary Optimization (QUBO) and showed how the number of resources (qubits) can be reduced. For example, to solve a seven-node TSP, the authors used 43 qubits.  
Another study on the TSP using the QAOA was reported [26]. A 48-node TSP was examined by partitioning it into smaller problems (9 nodes for the 48-city problem) and solving the sub-problems on a D-wave quantum computer. While optimal solutions were obtained for the smaller problems, only an approximate solution was achieved for the 48-node problem.  
In short, the current approaches to partitioning the TSP to find the optimal solution using quantum computers reported limited success for small TSPs. This was our primary motivation to develop a new approach that can handle large-scale problems by a partitioning approach such that individual partitions can be optimized on a quantum computer, and when these optimized sub-graphs are combined, the resulting optimization is near optimum. Note that a hybrid approach involving quantum computers and classical computing is needed because the number of qubits needed to solve a combinatorial optimization problem is O(\(n^{2}\)). Solving TSP instances containing over a few dozen nodes is beyond the scope of the current and near-future quantum computers. For example, a 1000-node TSP optimization will require a 1 million-qubit quantum computer. Thus, a hybrid approach consisting in dividing the problem into sequences of smaller TSP sub-graphs and then further optimizing the resulting individual optimized sub-graphs is the only viable solution in the near future.