esa-sceva/satcom-chunk-collection huggingface dataset:
# Relative Stability of Small Silver, Platinum, and Palladium Doped Gold Cluster Cations
## 4 Discussion
In Figure 5, a similar DOS analysis is presented for \(N=4\). Au\({}_{4}\)+ and AgAu\({}_{3}\)+ have very similar DOS. In both cases the HOMO corresponds to a singly occupied MO of delocalized character (\(\alpha\)-state) with the 1P\({}_{\mathrm{x}}\) symmetry. In addition, there is a doubly occupied MO resembling the 1S eigenstate. The lowest unoccupied molecular orbital (LUMO) of the clusters is now the \(\beta\)-state of 1P\({}_{\mathrm{x}}\) symmetry. Therefore, Au\({}_{4}\)+ and AgAu\({}_{3}\)+ have three itinerant electrons. The DOS of PtAu\({}_{3}\)+ and PdAu\({}_{3}\)+ is remarkable; both clusters have only the 1S-type delocalized MOs below the HOMO, implying that there are in total only two delocalized electrons. As shown in the figure, the 1P\({}_{\mathrm{x}}\) and 1P\({}_{\mathrm{y}}\) MOs are empty. For \(N=4\), neither Pt nor Pd contributes to the electron delocalization, contrary to the \(N=3\) case. PtAu\({}_{3}\)+ and PdAu\({}_{3}\)+ are clusters with closed electronic shells, as PtAu\({}_{2}\)+ and PdAu\({}_{2}\)+ are  
Figure 4: Total density of states of the clusters composed of \(N=3\) atoms. Projections into atomic states are presented in black for Au(d), in red for Au(\(sp\)), in blue for \(M\)(d), and in green for \(M\)(\(sp\)). The molecular orbitals of delocalized character extending over the entire cluster volume are plotted and labeled based on their nodal character (1S and 1P\({}_{\mathrm{x,y}}\)). The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) states are labeled as well. In the case that the cluster is open-shell, both \(\alpha\) and \(\beta\) orbitals are plotted. The final analyzed case (size \(N=5\)) is presented in Figure 6. As expected, \(\text{Au}_{\text{S}}\)+ and \(\text{AgAu}_{\text{4}}\)+ are clusters with closed electronic shells, since each atom delocalizes one valence electron. In both cases there are two doubly occupied MOs of delocalized character which resemble the 1S and \(1\text{P}_{\text{x}}\) eigenstates. This is similar to the cases of \(\text{PtAu}_{\text{4}}\)+ and \(\text{PdAu}_{\text{4}}\)+, which also have two doubly occupied MOs of delocalized character. For \(N=5\), Pt and Pd delocalize one electron in order to close the \(1\text{P}_{\text{x}}\) electronic shell of the clusters, thereby gaining stability. This analysis shows an a priori unexpected behavior of the Pt and Pd dopants on the Au clusters in the \(N=3\)-5 size range. The dopant atoms delocalize one electron at \(N=3\) and \(N=5\), but none at \(N=4\), allowing all the doped clusters to have closed electronic shells.
# An IVTIFN-TOPSIS Based Computational Approach for Pipe Materials Selection
## 1 Introduction
ELECTREmay couple with AHP to rank alternatives depending upon their outranking relationships [21]. However, the methods fail to explain the information that lies in the differences of ranking scores [22]. PROMETHEE fills such gap by using different scores to indicate the degree of preference among alternatives [23]. AHP, ELECTRE and PROMETHEE are based upon pairwise comparison among attributes to rank alternatives, which is time consuming in computation, especially when the number of alternatives or attributes enlarges in the decision-making process [24; 25]. TOPSIS, VIKOR and COPRAS are comparatively simple, systematic, and logical for decision makers to obtain the optimal option when facing a number of competing criteria [26]. TOPSIS and VIKOR are similar but different from their normalizations in selected indicators [27]. The latter focuses on a compromise solution between individual utility and group utility, by which the alternatives ranking may be deviated due to the differences in subjective weightings [28; 29]. Similarly, COPRAS is identified by using the utility degree to rank alternatives, which may cause results sensitive to the process of normalization [30]. TOPSIS is more adaptive to qualitative and quantitative information, by discriminating alternatives in terms of their Euclidean distances from the ideal solution [31; 32]. It has been identified as reliable and efficient in a number of studies on materials selection, for its easy computation and clear trade-offs among various criteria [33]. In addition, TOPSIS is inherently effective to couple with fuzzy set to tackle the uncertainty involved in decision making on materials selection [34; 35]. Engineering designers usually express their preference or judgment in a linguistic environment, which may result in vagueness [36]. The TOPSIS is unable to deal with such vagueness, which calls for improvement [37]. Fuzzy set provides the insight into TOPSIS development, by transforming the linguistic information into fuzzy numbers, to indicate decision maker's preference and judgment [38; 39]. However, linguistic variables in an ordinary fuzzy set are not clear enough to express the decision maker's preference by using an exact numerical value of either 0 or 1 [40; 41]. In such a case, this study applies intervaled trapezoidal intuitionistic fuzzy number (IVTIFN) to handling ambiguity in linguistic information, by which its predetermined numerical interval may better specify the fuzzification in a fixed bounded limit [42]. This study provides a MCDM-based computational approach, which couples IVTIFN with the TOPSISto aid engineers in the selection of commercially available materials. Mahalanobis distance is incorporated into IVTIFN-TOPSIS, to discriminate similarities of alternative materials by eliminating the highly correlated decision criteria. An illustrative case example is given to demonstrate its actual application. The study is expected to provide insight into sustainable design of waste-water piping system, ultimately to improve its sustainability.
## 2 Materials and Methods
### Heuristic Optimization Algorithms
#### 2.1.2 Mathematical Description for PSO  
This algorithm can optimize processes with \"n\" variables, having the searching space defined by these dimensions. At a certain time-step, defined by \"k\", the position of a certain particle can be determined using Equation (1) [14]:  
\[x_{k+1}^{i}=x_{k}^{i}+v_{k+1}^{i}, \tag{1}\]  
the speed can be calculated as follows, using Equation (2) [14]:  
\[v_{(k+1)}^{i}=v_{k}^{i}+c_{1}r_{1}(p_{k}^{i}-x_{k}^{i})+c_{2}r_{2}(p_{k}^{g}-x_ {k}^{i}), \tag{2}\]  
while the parameters from Equation (3) represent the cognitive scaling factors and are usually chosen as constant values:  
\[c_{1}=c_{2}=2, \tag{3}\]  
Figure 5: Illustration of PSO algorithm principles and mathematical description.  
with the variables' meanings as follows:  
* \(x^{i}_{k+1}\)--Position for particle number \"i\" at time-step \"k + 1\";
* \(v^{i}_{(k+1)}\)--Particle velocity;
* \(p^{i}_{k}\)--Personal best for particle \"i\";
* \(p^{8}_{k}\)--Global optimum point at time-step \"k\";
* \(r_{1}r_{2}\)--Uniformly distributed random variables, values between 0 and 1.
# A Pilot Study of Stacked Autoencoders for Ship Mode Classification
## 4 Approach
### Evaluation Metrics  
This study selected accuracy, TPR, FPR, and MCC as the evaluation metrics to compare the performance of the models. The accuracy is the ratio of the number of data points that the multi-class classification model correctly classifies to the overall number of data points. TPR is the level at which the actual correct answer is clearly predicted. FPR is used to evaluate multi-class classification performance. Lastly, MCC has the advantage of being able to express the confusion matrix of a multi-class classification model in a balanced way; it is also a balanced evaluation metric that is good for representing the performance of such models [34; 35]. MCC was considered to be the most important of all the evaluation metrics because this study presents research on a model that performs multi-class classification. The evaluation metrics can be expressed as shown in the following equations:  
\[\text{True Positive Rate }(\text{TPR})=\frac{TP}{TP+FN}\]  
\[\text{False Positive Rate }(\text{FPR})=\frac{FP}{FP+TN}\]  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}\]  
\[\text{MCC}=\frac{c\times s-\sum_{k}^{K}p_{k}\times t_{k}}{\sqrt{\left(s^{2}- \sum_{k}^{K}p_{k}\right)\times\left(s^{2}-\sum_{k}^{K}t_{k}{}^{2}\right)}}\]  
Here, \(TP\) is the number of true positives, \(TN\) is the number of true negatives, \(FP\) is the number of false positives, and \(FN\) is the number of false negatives. \(c\) is the number of samples correctly predicted out of the number of samples, and \(s\) is the number of samples. \(K\) is the total number of classes, and \(k\) is an individual class (a class from 1 to K). \(p_{k}\) is the number of times class \(k\) was predicted, and \(t_{k}\) is the number of times class \(k\) truly occurred.
# Enabling Heterogeneous IoT Networks over 5G Networks with Ultra-Dense Deployment--Using MEC/SDN
## 4 Energy-Aware and Latency-Aware Offloading Scheme
### Energy-Aware and Latency-Aware Offloading Algorithm for IoT Networks  
The proposed offloading algorithm consists of three offloading levels besides the local execution, as presented in Figure 4. The zero-level offloading, which corresponds to the local execution. This represents tasks that can be executed by the end devices without any need for the offloading process. This depends on the currently available resources of end-devices and the required QoS of the task. The first level of offloading represents
## 5 Discussion of the Results
### Federations to Share Storage Capacity  
After accomplishing the federations to share downlink opportunities, federations to share storage capacity were the target. At 12:35:26, Balloon A started to publish the availability of this service (Figure 19). This publication is received by Balloon B which requests the service to store 38 data packets. The request is accepted by Balloon A, which provokes the establishment of the federation. This situation is repeated up to four successful federations. The first federation remains open for 1 min and 13 s, the following one for 1 min and 25 s, the next one for 52 s, and the final one for 27 s. This decrease is caused by the degradation of the link quality. Specifically, the asymmetric link between Balloon A and B (as in previous cases).  
Although these difficulties, multiple data packets are exchanged between the balloons. Figure 20 presents the buffer fluctuation in each balloon during this scenario. Balloon B starts to transfer data to Balloon A, which enable a decrease to the buffer usage. Meanwhile, the buffer usage in Balloon A increases, because it is receiving the data from Balloon B. Please note that there are lapses of times that the buffer of Balloon A remains at the same usage, due to the break of the federation. Moreover, Balloon A does never reach the maximum capacity of the service that it has offered (i.e., 50 packets). For that reason, it remains publishing the service.  
Figure 17: Sequence of packets exchanged in federations with a balloon as a data relay.  
Figure 18: Buffer usage of Balloon A (blue) and Balloon B (red) in data packet unit with a balloon as a relay.  
During this scenario, 19 data packets are exchanged between the balloons using federations. Taking into consideration the duration of the different federations, the achieved throughput is 107.6 bps. The PER in this scenario is smaller than in the other cases, with 42.2% of transmitted packets lost. Taking this link quality, the resulting throughput is equivalent to 94.4% of the maximum throughput (Equation (6)). Please note that the current communications must be compared with the reference values as a downlink communication because in this case there is no intermediate node that relays data.  
Although the difficulties achieved to establish federations due to the poor quality of the link, multiple federations to share storage capacity have been achieved. These federations have enabled Balloon B to store its data into Balloon A. This exchange reduced the usage of Balloon B buffer, while the buffer in Balloon A was increasing. These results demonstrate that the proposed protocols can manage different types of services following the same interaction.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 4 Conclusions
Moreover, the proposed cell can be used together with ECC techniques to further enhance system reliability, and it functions not as a replacement for ECC but as a complementary approach. Therefore, the proposed EWS-16T cell is highly suitable for integration into compact platforms such as CubeSats and nanosatellites, where both high integration density and robust radiation tolerance are essential requirements. Conceptualization, S.-J.K. and S.-H.J. ; methodology, S.-J.K.; validation, S.-J.K.; data curation, S.-J.K.; writing--original draft preparation, S.-J.K.; writing--review and editing, S.-H.J. ; visualization, S.-J.K.; supervision, S.-H.J. ; project administration, S.-H.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: Data are contained within the article. The EDA tool was supported by the IC Design Education Center, Korea. The authors declare no conflicts of interest.
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 2 Datasets
### Quranic Text  
As Quran is a Holy Book, it is recited worldwide, and everyone wants to recite it correctly without any mistakes. [PERSON] et al. [51] review the Quranic NLP techniques, approaches used, tools, and datasets, and recitation via speech-recognition method. The techniques used in the paper are text preprocessing, text matching, clustering, classification, and speech processing. Quranic NLP work includes grammatical NLP analysis and semantic- and ontology-based technologies using BLSTM. The model took recitation of different reciters for training purposes, and a feature widely used for speech recognition, named mel-frequency cepstral coefficients (MFCCs), gives a 99.89% recognition rate for 3 s of recitation, which is far better than all of the other techniques.
# A Chaotic Secure Communication System Design Based on Iterative Learning Control Theory
## 3 Chaotic Secure Communication Design Based on ILC Theory  
This section presents our secure communication system design by employing the chaotic _logistic-map_ and by using the ILC framework as a tool to encrypt and decrypt a message. We are going to use its property for the estimation of the unknown time-varying parameter \(\theta(t)\).  
To begin with, let us refer to the main block diagram shown in Figure 1. Allow us to define the _transmitter_ block as:  
\[\dot{x}_{i}=\theta(t)\dot{x}_{i}+u_{i},\quad x_{i}(0)=x_{i-1}(T). \tag{6}\]The initial conditions \(x_{i}(0)=x_{i-1}(T)\) means that the end state of the preceding iteration becomes the initial state for the new one. Here, \(\theta(t)\) represents the message to be encrypted. In our design, we are using a two-channel communication system. The transmitted signal \(x_{i}(t)\) goes on one channel, and on the other travels the generated signal \(u_{i}(t)\) coming from the receiver (see Figure 1). Both signals have to be in _random-like_ behavior with the message encrypted in \(x_{i}(t)\). Then, \(\hat{\theta}_{i}(t)\) becomes the decrypted signal (an estimation of the transmitted message in \(\theta(t)\)). To complete our design for the transmitter section, let us define \(\xi_{i}=x_{i}(1-x_{i})\), a _mimic_ expression from the chaotic logistic-map.  
Now, _designing_ the _receiver_ block as:  
\[\dot{x}_{r} = -x_{r}+z(j),\quad x_{r}(0)=0.1, \tag{7}\] \[\dot{\theta}_{i}(t) = \mathrm{proj}(\hat{\theta}_{i-1}(t))-\xi_{i}e_{i},\quad\hat{ \theta}_{-1}=0,\] (8) \[e_{i}(t) = x_{r}(t)-x_{i}(t),\] (9) \[u_{i}(t) = ke_{i}(t)+f_{r}-\hat{\theta}_{i}(t)\xi_{i}, \tag{10}\]  
where \(z(j)\) is realized by employing the well-known discrete-time _chaotic_ logistic-system:  
\[z(j+1)=4z(j)(1-z(j)),\quad z(0)=0.1\ (\mathrm{we\ set\ }z(0)=x_{r}(0),\mathrm{ but\ it\ does\ not\ matter}),\quad j=0,1,2,3,\cdots. \tag{11}\]  
The output of the logistic-map \(z(j)\) (utilized as the reference signal \(r=r(t)\) to \(f_{r}\)) is assumed being performed having a _zero-order-holder_ device (ZOH), \(T\) being the time duration between discrete-time samples. Recall that the \(ZOH\) device is a transformation mechanism to translate a _discrete-time_ signal to a piece-wise _continuous-time_ one (see, for instance, Chapter 1 in [16]).  
Stability issueThe stability issue of the overall closed-loop communication system is warranted by the results given in [9] and stated in Section 2, including its robustness property.
## 3 Emerging and State-of-the-Art Technologies
### Reconfigurable Intelligence Surface (RIS)
Table 1 lists some notable RIS works that span both passive and active architectures across a wide frequency range from sub 6 GHz to the terahertz band. The table summarizes key characteristics including the type of RIS (passive, active, or hybrid), the number of elements, physical dimensions, underlying manufacturing technology, and operating frequency range, as well as some reported performance metrics. Figure 4: Stacked reconfigurable intelligent metasurface antenna, [71]. Figure 3: The operation of a reconfigurable intelligence surface in a 6G society. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Reference** & **Type** & **\# Elements** & **Size** & **Technology** & **Frequency** & **Performance Metrics** \\  & & & & & **Range (GHz)** & \\ \hline
[60] & Active & 400 (20 \(\times\) 20) & 10 \(\times\) 10 cm & 6-player PCB (Metorawase 8000 + FR4), PIN diodes & 27.5-29.5 & 30 dB diffusivity; 25 dB gain, \(<\)8 W, \(\pi\) phase \\ \hline
[61] & Active & 100 (94 reflective + 6 sensing) & 50 \(\times\) 50 mm & LTCC + SIV + Rotman lens, actors, MCU & 27.5-28.5 & DoA tracking, beam steering, \(<\)5 dB loss, 1-bit control \\ \hline
[65] & Passive & Single unit (scalable) & 20 \(\times\) 20 mm & Rogers 5880 + FR4, varactor diodes & 5-5.5 & 360” phase shift (TE/TMD, 15-6 dB reflections long depending on polarization \\ \hline
[67] & Passive & - & 12\(\times\)12 mm per unit & Sawtooth metasurface on FR4 & 4.2-12.5 & 2 \(\times\) 360” phase shift, beam redirection, tested at 10 GHz \\ \hline
[68] & Active & 196 (14 \(\times\) 14) & 362 \(\times\) 381 mm & Multiport system with 3-bit varactors & 2.6 & Beam steering (22\({}^{\circ}\)/34), 94% simulation time reduction, low RMSE \\ \hline
[71] & Active + Passive & Theoretical analysis & Theoretical analysis & Stacked metasurfaces: passive (phase) + active (amplifier-based amplitude) & \(f_{0}\) & Wave-domain beamforming, sum-rate optimization, ZF beamforming, hybrid SIM \\ \hline
[71] & Passive & 576 (24 \(\times\) 24) & 720 \(\mu\)m \(\times\) 720 \(\mu\)m & Graphene on sapphire and gold backplane & 1000–5000 & Digital and analogical beam steering, mutual coupling optimization \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of RIS characteristics from recent works.
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 6 Performance Evaluation  
To determine the feasibility of our ZSM-based framework, we extracted various metrics for both proactive and reactive stages as part of a common PoC.  
For validating the proactive phase, we measured the time taken by the framework to deploy and configure an SSLA as an E2E 5G security slice, as outlined in Section 4.2. We provided: (i) time measurements from an E2E perspective, and (ii) detailed time measurements per phase (orchestration, deployment, and configuration enforcement) within each SMD. For the reactive phase, we triggered a cryptomining attack as described in Section 4.3. We considered (iii) the number of false positives and false negatives in the STA, (iv) the E2E and SMD Mean Time To Resolve (MTTR) the incident, (v) the Time to Detect, and finally (vi) the AMF Downtime.
# An Alternative Series Solution for Free Vibration Analysis of Asymmetric L-Shaped Membranes
## 1 Introduction
Such a 3-SDD strategy divides the entire computational domain into three rectangular subregions (see Figure 1a) instead of the two subregions used in the 2-SDD approach (see Figure 1b). While the primary distinction lies in the number of subregions, the implications of this difference are significant and wide-ranging. Figure 1: Cont. Before delving into the complexities of multi-segmented composite L-shaped membranes, it is imperative to understand the fundamental building blocks first. The three-segmented L-shaped membrane represents the minimal unit that captures the essential characteristics of these more complex structures. While [PERSON] et al. (2018) have previously investigated two-segmented L-shaped membranes, it is necessary to recognize that this configuration is merely a particular case of the three-segmented L-shaped membrane. The simplified models developed for two-segmented membranes are not fully applicable to the three-segmented case, highlighting the need for dedicated research in this area. The actual superiority of the 3-SDD strategy becomes evident when dealing with L-shaped membranes featuring elongated horizontal and vertical arms characterized by high aspect ratios in the subregions. In such cases, the 3-SDD strategy necessitates the satisfaction of fewer boundary conditions than the 2-SDD strategy. The 3-SDD strategy captures the interactions between the membrane segments more faithfully, especially in regions where the membrane geometry changes abruptly, such as at the junction of the L-shape (i.e., region 2 in Figure 1a). Consequently, the 3-SDD strategy is expected to yield more accurate computational results for asymmetric L-shaped membranes with slender arms than its 2-SDD counterpart. This enhanced accuracy is not merely a marginal improvement but represents a leap in our ability to model and predict the behavior of these sophisticated membrane structures. The improved precision is valuable in applications where even minor deviations in performance can have significant implications, such as in susceptible pressure sensors or precision MEMS devices ([PERSON] et al., 2018). From the perspective of advancing microfabrication techniques toward more flexible and tunable designs, the study of the 3-SDD strategy serves as a critical stepping stone. This research is necessary and pivotal in laying the groundwork for understanding multi-segmented composite L-shaped membranes. In the future, researchers can uncover fundamental principles governing more composite membrane structures (e.g., E-, I-, T-, and pi-shaped membrane-based sensors). The proposed 3-SDD strategy allows for a systematic exploration of how material properties, segment dimensions, and their interactions influence the overall performance and tunability of the device. Figure 1: Geometric layout of the asymmetric L-shaped membrane: (**a**) the 3-SDD strategy adopted in the present study; (**b**) the 2-SDD strategy employed by [PERSON] et al. (2018).