esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluation and Results
### Results
#### 3.3.1 Optical Wavelength Dither  
To evaluate the wavelength dither, we first manually adjusted the CLC system to roughly align the sideband with the MRR resonant peak. Next, we let our CLC system lock the wavelength more precisely, while adjusting the frequency and amplitude of the modulation signal for the minimum working dither. Note that the optical power of the sideband is greater than that of the carrier, which can be seen on its left. Whether this actually happens or not depends on several factors, such as the RF gain of the MZM (also dependent on its bias setting), the ER of the MRR, the polarization control, etc. However, this is a requirement for the system to work as otherwise the system would tend to align the carrier--rather than the sideband--with the MRR's resonance. After meeting this requirement, we performed three measurements, which we show graphically in Figure 14 below. We captured the instantaneous optical spectrum (red solid line) and, given the time constant of our CLC system, we performed a max-hold optical spectrum measurement with a duration of \(T=300\) s \(\gg\tau_{f}\) (blue dash-dotted line)--significantly longer than that of the integrator in the CLC system. To confirm the alignment with the MRR's resonance, we also captured the input-drop MRR OTF and included it in the same graph (orange dashed line). The resulting dither was evaluated at \(\varepsilon_{\lambda}=\left(B_{\mathrm{blue}}-B_{\mathrm{orange}}\right)/2 \approx 0.0038\) nm (\(\sim\)466 GHz).  
The resonant wavelength in Figure 13 differs from that in Figure 14. This is because before the latter measurement was performed the system had been running for some time (and therefore resonance shifting) to confirm the successful long-term locking.  
The locking state was achieved and the measurements performed with the CLC set to a frequency of \(f_{\triangle}\approx 1.5\) Hz, as can also be seen from the Figure 15. This figure shows the oscillogram with voltage measurements at several points of the CLC circuit. The red dashed line presents the voltage signal \(u_{\mathrm{in}}\) controlling the VCCS, the orange dash-dotted line shows the rectangular signal with the phase corrected and fed to the input of the mixer, and finally the blue solid line presents the signal measured after the buffer amplifier at the  
Figure 14: OTF of the MRR6 from input to drop port, the instantaneous spectral power density measured at the drop port and the corresponding max-hold measurement.  
input of the CLC. This last is actually the feedback signal that is essential for our concept of wavelength locking.
# Development of a Social DTN for Message Communication between SNS Group Members
## 3 How to Build a Social DTN
### DTN Architecture  
The proposed DTN architecture is shown in Figure 2. The ad hoc communication function uses Nearby Connections API [13] as well as Bluetooth and Wi-Fi Direct to establish a communication link between terminals that share the same community token. After that, a route is established and data communication is started based on the DTN routing protocol.  
Nearby Connections API [13] provides several strategies. We have selected \"P2P_STAR,\" which is a one-to-many P2P strategy and offers high communication speed. We have used a mode in which there are \(N\) Discoverers (children) for each Advertiser (parent). The Advertiser broadcasts a communication request message, specifying two parameters: Strategy and Service ID. Service ID is an application-unique value used to identify communication made by the same application at Nearby Connections API [13]. It is set by the application developer. The Discoverer receives a broadcast message that has the same Strategy and Service ID parameters as those specified by the Advertiser. It also sends a communication request message. Both the Advertiser and the Discoverer check the communication request and Service ID sent from the other terminal, and if they both give permission,  
Figure 1: Example of using a social DTN (delay, disruption, disconnection tolerant networking). (**a**) The left figure shows an environment, such as an airport, where the user (e.g., a visitor from abroad) can access the Internet; (**b**) The right figure shows an environment, such as on the street, where the user cannot access the Internet.  
a communication link is established between the two terminals. The communication phase moves to the phase in which data, such as text and file, can be exchanged. After that, data exchange can continue until one of the two terminals disconnects the communication.
# Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review
## 4 Methodologies, Strengths, and Weaknesses  
The specifics of the most popular ML and DL algorithms used to create an effective DDoS detection model are described in this section, along with basic techniques for AI-based DDoS detection. Both supervised and unsupervised methods are used in ML and DL. In supervised algorithms, data need to be labeled prior to use. Unsupervised algorithms, on the other hand, use unlabeled data to extract important characteristics and details. The methodologies, advantages, and disadvantages of studies using these approaches are summarized in Table 3. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference** & **Methodology** & **Strengths** & **Weaknesses** \\ \hline [PERSON] et al. [43] & Used BAT algorithm with Ensemble Method for Optimization. & When used in an ensemble setting, several ELMs showed good performance. & Used outdated datasets, including Kyoto, NSL-KDD, and KDDCup99. Additionally, the model’s detection accuracy for the U2R attack class was lower. \\ \hline [PERSON] et al. [44] & Utilized RF with Non-Symmetric Deep Auto Encoder & Presented a non-symmetric deep AE and RF classifier-based DDoS detection, which lowers the model complexity & The model was examined with outdated datasets, and its performance on the minority R2L and U2R classes was on the low side. \\ \hline [PERSON] et al. [45] & Employed a Particle Swarm Algorithm and a Fast Learning Network & The suggested model outperformed other FLN-based models using several additional optimization strategies & Outdated dataset used. Additionally, fewer training data led to a lower detection rate. \\ \hline [PERSON] et al. [46] & Utilized SVM and a Sparse Auto Encoder & SVM was effectively used as a classifier with SSAE for feature extraction to identify DDoS attacks & The model was evaluated using an outdated dataset; while the model’s detection rates for U2R and R2L attacks were respectable, they were lower than for the other attack classes in the dataset. \\ \hline [PERSON] et al. [47] & Comparison of several ML/DL-based IDS models & Used a GPU-integrated testbed to compare ML/DL-based DDoS detection methods & The flaws were evaluated using an earlier dataset called NSL-KDD. \\ \hline [PERSON] et al. [48] & A self-taught learning model was employed using an autoencoder and SVM & The effective notion of self-taught learning based on Sparse AE and SVM was proposed as a DDoS detection model & An older dataset called NSL-KDD was used. Additionally, no outcomes were provided regarding the model’s effectiveness against minority attack classes \\ \hline \end{tabular}
\end{table} 
Table 3: Methodologies, strengths, and weaknesses of different studies using ML/DL for DDoS detection. Figure 4: Taxonomy of machine learning-based and deep learning-based DDoS detection systems.
# Design and Analysis of a Novel Microspeaker with Enhanced Low-Frequency SPL and Size Reduction
###### Abstract  
In the era of multimedia devices, smartphones have become the primary device for consuming multimedia content. As technological developments have facilitated a more immersive multimedia experience, enlarged displays and the use of several sensors have limited the allowable size of microsp peakers. Although sound plays an important role when consuming multimedia content, the limited space for microsp peakers in modern devices leads to poor acoustic performance, especially at low frequencies. To address this issue, this paper proposes a novel microsp peak structure that enhances the low-frequency sound pressure level (SPL), while also featuring reduced exterior dimensions. The structure was designed and analyzed using 3D finite element analysis. Through coupling analysis, the simulation results were verified on the basis of the experimental results. The novel microsp peaker has one outer magnet surrounding the entire coil, unlike in prototype microsp peakers, which have two outer magnets. The gap between the top plates and coil is reduced, and a new type of coil is introduced for the purpose of increasing electromagnetic force. The samples were manufactured, and their SPLs were tested in an anechoic chamber. The experimental results prove that the proposed microsp peaker offers an improved SPL at low frequencies compared with prototype microsp peakers.  
microspeaker; sound pressure level; structure design; coupled analysis; multiphysics Article
## 2 Related Work
[35] proposed an automatic road damage detection and classification based on Convolutional Neural Network (CNN) using an R-CNN model. In the last two years, some studies have used advanced algorithms such as a single stage that uses a single CNN, such as YOLO, to predict the class and location of damage directly: for instance, [PERSON] and [PERSON] [36], [PERSON] et al. [37], [PERSON] [38], [PERSON] et al. [39], [PERSON] [40] and [PERSON] [41]. Although many studies have proposed approaches to automate the detection of road damages, several problems remain, and there is still room for more improvement. New methods could be proposed to classify more types of road damage more precisely, especially those present in the Kingdom of Saudi Arabia, whose nature of damage may differ from other countries from which the available datasets were collected in terms of geography and climate. Table 1 summarizes the studies conducted to detect and classify road damage using ML and DL methods, and as shown, the most used ML method was SVM. CNN was the most commonly used for DL. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Research** & **Method** & **Dataset** & **Country** & **Classes** & **Results** \\ \hline
[7] & Random structured forests & 11,838 images & China, France & Crack/No crack & Precision at up to 95\%, Recall up to 95\%, F1 score up to 95\% \\ \hline
[19] & Histogram, [PERSON]’s thresholding & Internet & undefined & Crack/No crack & Precision 77.72\%, F1 score 77.82\%, F1 score 77.70\% \\ \hline
[20] & Fuzzy c-means & 75 images & Eldoret Town, Kenya & Pothole/without pothole & Mean of errors 8.5\%, SD of errors 4.9\% \\ \hline
[21] & SVM & Undefined & undefined & Crack/No crack & Precision of 88.85\%, Recall of 99.04\%, F1 score is 93.31\%. \\ \hline
[22] & Bagged Trees, SVM & 400 images & Tehran, Iran & Transversal, longitudinal, diagonal, block & Accuracy of 93.86\% \\ \hline
[23] & DT & 102,400 images & undefined & Transverse, longitudinal, and alligator & Accuracy of Detecting 88\%, Classification 80\% \\ \hline
[8] & DCNN & 500 images & USA & Crack/No crack & Precision 0.8696 Recall 0.9251, F1 score 0.8965 \\ \hline
[9] & CNN (VGG16) & 851 images & Denmark & Crack/No crack & Accuracy 92.27\% \\ \hline
[27] & Alexnet & 2173 images & Undefined & Crack/No crack & Accuracy 94\% \\ \hline
[28] & CNN & 40,000 images & Turkey & Crack/No crack & Accuracy 99.92\% \\ \hline
[29] & CNN, AlexNet, Xception & 1176 images & India & Potholes, concrete, rutting, line, and crack & CNN and AlexNet achieved an accuracy of 86\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of ML and DL methods.
# Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks
## 3 The Attention-Encoding-Based Graph Convolutional Network Model (AEGCN)
### Semantic Coding
#### 3.1.3 Multi-Head Attention  
Multi-head attention (MHA) [22] is an attention function that can be performed in parallel subspaces. In this paper, multi-head self-attention and multi-head interactive attention are employed to model different goals. This paper defines a Key sequence \(k=\{k_{1},k_{2},...,k_{n}\}\) and a Query sequence \(q=\{q_{1},q_{2},...,q_{n}\}\) according to our specific task. The attention value is obtained by calculating the attention distribution with Key and attaching it to Value. Since keys and values are often the same in the application field of NLP, here Key = Value. Then, an attention function projects Key and Query to an output sequence:  
\[A\text{ttention}(k,q)=soft\max(f_{m}(k,q))k \tag{1}\]  
\(f_{m}\) is the function used to calculate and study the semantic relevance between \(q_{j}\) and \(k_{i}\):  
\[f_{m}(k_{i},q_{j})=\tanh([k_{i};q_{j}]\cdot W_{a}) \tag{2}\]  
\(W_{a}\in R^{2d_{hit}}\) is the learning weight matrix. MHA is able to learn different scores of \(n\_head\) in parallel subspaces, and the parameters between heads are not shared because the values of \(q\) and are constantly changing. The outputs of \(n_{head}\) are concatenated and projected to the specific hidden dimension \(d_{hid}\) by:  
\[MHA(k,q)=[o^{1}\oplus o^{2}\oplus...\oplus o^{n_{head}}]\cdot W_{m} \tag{3}\]  
\[o^{h}=Attention^{h}(k,q) \tag{4}\]  
where \"\(\oplus\)\" represents the vector concatenation, \(W_{m}\in R^{d_{hid}\times d_{hid}}\), \(o^{h}=\left\{o_{1}^{h},o_{2}^{h},\dots,o_{m}^{h}\right\}\) is the output of the \(h\)-th head attention, and \(h\in[1,n_{head}]\). Multi-head self-attention (MHSA) is a special situation of MHA whose input \(q=k\). Given the context hidden state \(H^{c}\) and the target word hidden state \(H^{t}\), AEGCN can derive semantic encoding of the context and the target words \(H^{cs}\), \(H^{ts}\) as follows:  
\[H^{cs}=MHA(H^{c},H^{c}) \tag{5}\]  
\[H^{ts}=MHA(H^{t},H^{t}) \tag{6}\]  
where \(H^{cs}=\left\{h_{1}^{cs},h_{2}^{cs},...,h_{n}^{cs}\right\}\in R^{d_{h}\times n}\), \(H^{ts}=\left\{h_{1}^{ts},h_{2}^{ts},...,h_{m}^{ts}\right\}\in R^{d_{h}\times m}\), and \(d_{h}\) is the dimension of MHSA.
## 4 Discussion
The study demonstrated a significant increase in maxillary intercanine width with SRPE compared to RME (mean \(\pm\) SD = 6.0 \(\pm\) 4.8 mm vs. 3.1 \(\pm\) 2.9 mm; \(p\) = 0.00), highlighting the advantage of using SRPE in expanding the anterior dental arch. This is consistent with previous findings [12], when similar increases in intercanine width were observed with the use of bonded palatal expanders. The difference in performance between SRPE and RME may be attributed to the lateral slides in the SRPE, which favor anterior expansion. Both devices showed a similar increase in maxillary intermalor distance (RME: 7.3 \(\pm\) 3.4 mm; SRPE: 7.6 \(\pm\) 5.1 mm), consistent with findings from [PERSON] et al. [22], who reported no significant differences in intermalor expansion between bonded and banded expanders. The significant change in mandibular arch dimensions post-treatment aligns with the results found by [PERSON] et al. [23] only for the SRPE group, suggesting that the mandibular response to maxillary expansion can hopefully be achieved. Skeletal variables, including vertical dimensions, did not show significant changes between the two groups. The presence of significant effects on vertical skeletal variables (SN-GoGn) does not support previous studies [11; 12; 24], which reported no long-term changes in mandibular plane angle following RME. These findings could be crucial, as the vertical effects of maxillary expansion remain controversial, with some studies suggesting temporary increases in vertical height due to overexpansion impacts [6; 10]. Our study found no significant changes in sagittal variables (ANB, SNA, and SNB) in either group, supporting the hypothesis that RME primarily affects the transverse plane, with minimal impact on anteroposterior skeletal relationships. However, the lack of long-term control must make us cautious about the stability of the vertical skeletal dimension provided by the two devices. These results are consistent with [PERSON] and [PERSON] [25], who noted, in original first studies, that craniofacial growth and morphogenesis could be stabilized by soft tissue constraints, mitigating skeletal changes from RME. These observations are still valid if we also look at the effect on the sagittal airway changes induced by the rapid palatal expansion reported by other authors [26; 27]. Despite the short-term benefits observed, the study acknowledges limitations, including the lack of long-term follow-up data to assess the stability of dental and skeletal changes. Future research should aim to evaluate the long-term retention of the effects of SRPE and RME, especially considering the potential for relapse in dental arch widths. The potential bias of single-operator measurements and the growth variability of our patient group could be further sources of research, also through the use of 3D images.
## 6 Discussion  
Resilience evaluation involves multiple factors, and PSR theory is commonly used to analyze the influencing factors in three dimensions: pressure, state, and response. The deterministic methods used to calculate the final resilience based on this theory can capture resilience relatively comprehensively and reflect both positive and negative feedback effects of resilience under pressure disturbances and emergency responses [37; 53; 54]. However, these studies often use broad statistical data as calculation indicators, making capturing resilience under specific event impacts challenging. In addition, some studies have not fully considered uncertainty in the resilience evaluation process, and there are fewer examinations of correlations between resilience-influencing factors.  
In the road traffic field, resilience research mainly constructs models focused on functional changes in roads and relevant variables as resilience attributes [17; 18]. However, these models cannot demonstrate the multidimensional effects of pressure disturbances, state resistance, and response recovery that roads face during emergency events. Furthermore, measuring dynamic changes in resilience has been constrained by using static Bayesian networks or rough-grained indicators.  
This study proposes a novel road resilience modeling and evaluation method, combining domain knowledge with historical data on emergency events using PSR and DBN theories. Cross-validation and sensitivity analysis verified the model's accuracy and examined key factors affecting resilience.  
However, this paper acknowledges that some limitations of the current method cannot be ignored and that there is room for improving model accuracy and application scenarios. Data quality and accuracy may be improved by strengthening data collection methods, especially for manual text records. A more refined classification of node resilience status could achieve a more precise resilience measurement. Additionally, future work could focus on measuring resilience for a particular type of severe disaster event, such as a hazardous chemical accident, through a more targeted Dynamic Bayesian Network model.
## 2 Materials and Methods
### Thermal Debinding and Sintering
The anisotropic shrinkage of series Linear 0 100% YES and Linear 90 100% YES indicates that the shrinkage is dominant in the deposition direction. In series Linear 0 100% YES, the strands are deposited along the length, and in Linear 90 100% YES, the strands are deposited along the width; therefore, the maximum shrinkage occurred in length and width, respectively. For series Linear 0/90 100% YES, the number of strands deposited along the width is higher than the number of strands deposited along the length; thus, these specimens shrunk more in width than in length. An infill pattern with alternating deposition along the length and width in each layer can create a more isotropic shrinkage as observed for series Diagonal 100% YES, where the shrinkage in length and width is not significantly different. These shrinkage results are crucial, and they must be considered  
Figure 7: Sintered specimens with cover and without and different infill degree (**a**) 25%, (**b**) 50%, and (**c**) 75%. (**d**) Comparison between sintered and printed part. The dotted rectangle highlights the delamination between the infill and the perimeter. when designing the 3D model in CAD software by oversizing the dimensions by the respective percentages to match the tolerances required. Anisotropic shrinkage is not unique for specimens prepared by MEX, and it has been observed for sintered specimens prepared by different shaping mechanisms, such as tape casting and MIM. During all these shaping processes, particles orient in the direction of flow. The copper particles used in this investigation are not entirely round, as shown in Figure 1b; therefore, the ellipsoidal particles present in the powder rotate so that their longest axis is parallel to the flow direction. Moreover, it has been shown that ellipsoid particles shrink more in the long axis because larger necks form rapidly in the contact area along the short axis since less material is required to close the inter-particle pores [71]. Sintering theory and simulations have shown that the shrinkage rate is inversely proportional to the square of the neck size; thus, large necks shrink more slowly [72]. As such, more shrinkage can be expected in the direction of deposition during MEX. Please note that the shrinkage would also depend on the sintering conditions, such as the sintering time and temperature, increasing the final density of the parts and modifying their microstructure.
###### Abstract  
Demand for electricity is constantly increasing, and production is facing new constraints due to the current world situation. An alternative to standard energy production methodologies is based on the use of renewable sources; however, these methodologies do not produce energy consistently due to weather factors. This results in a significant commitment of the user who must appropriately distribute loads in the most productive time slots. In this paper, a comparison is made between two methods of predicting solar energy production, one statistical and the other meteorological. For this work, a system capable of presenting the scheduling of household appliances is tested. The system is able to predict the energy consumption of the users and the energy production of the solar system. The system is tested using data from three different users, and the mean percentage of consumption reduction is about 77.73%. This is achieved through optimized programming of appliance use that also considers user comfort.  
optimization; renewable energy; modeling; mathematical programming +
Footnote †: journal:  
0000-0001-9991  
energy for all\". Specifically, the results carried by this work could be an improvement of goal 7.2 \"By 2030, increase substantially the share of renewable energy in the global energy mix\" because optimization of household appliance scheduling could lead to a reduction of billing costs and a reduction in the power demands on the supply system.  
This work proposes a novel and integrated system able to compute new scheduling for household load usage without affecting customer comfort. Hence, the proposed system increases energy efficiency by making full use of renewable energy resources. In this regard, a comparison between two methods to predict power production is presented in order to select the best method to achieve lower billing costs.  
This paper is organized as follows. Section 2 provides the literature review. Section 3 explains extensively the methods used to build the systems; Section 4 concerns the dataset used to build the experiments; and Section 5 presents the experiments and their results. Finally, the conclusions are presented in Section 6.