esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Discussion
### Analysis of Cross-Word Embeddings
#### 4.2.2 Sentence-Translation Retrieval  
For experiments on sentence-translation retrieval, we use the same evaluation set described in Section 3.1, consisting of 2000 triples of aligned English-Korean-Japanese sentences.  
The final embeddings of words in a query sentence can be acquired by calculating the weights of the encoder's self-attention layers, and the query sentence embedding can be  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**MASS Encoder**} \\
**Models** & **JA \(\rightarrow\) KO** & **KO \(\rightarrow\) JA** & **EN \(\rightarrow\) KO** & **KO \(\rightarrow\) EN** \\ \hline Unsupervised NMT & 13.49 & 7.31 & 4.92 & 1.27 \\ Supervised NMT & 8.37 & 4.83 & 3.61 & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performances of word translation.  
acquired by averaging those final embeddings. A sentence translation can be carried out by retrieving a target sentence whose embedding is nearest to that of a query sentence.  
We use 2000 queries of Japanese and English and 200,000 Korean candidates, including 2000 target sentences. Table 5 presents the results and the performances are also estimated by precisions, which are the ratio of correctly translated sentences among the total 2000 sentences. In Table 5, 'Initial embeddings' refers to word embeddings prior to the first layer of an encoder, while 'Final embeddings' refers to word embeddings recalculated through the encoder's layers.  
Like the word-translation task, the sentence-translation task from Japanese to Korean outperforms that from English to Korean.  
In addition, as easily guessed, the precisions of the supervised NMTs are higher than those of the unsupervised NMTs. The precision of Japanese to Korean rose to 76.85% when supervised learning is adopted in the pre-trained models. The precision of sentence translation from Japanese to Korean in unsupervised learning goes up to 20.90% accuracy, although the NMT model is never trained with any parallel sentences.  
The precisions of the final embeddings were much better than those of the initial embeddings in the case of Japanese to Korean, regardless of how the NMT systems are trained. This indicates that the encoder's internal layers are well trained enough to appropriately move word embeddings in a vector space, taking into account the context in which they are used.  
Only final embeddings of supervised NMT seemed to work for sentence translation from English to Korean. This is analogous to the results in Table 1, in which a supervised learning process is indispensable for an English-Korean NMT system.
# A Distributed Terahertz Metasurface with Cold-Electron Bolometers for Cosmology Missions
## 1 Introduction  
A cold-electron bolometer (CEB) is a cryogenic device with a tiny absorbing normal metal stripe connected to the two superconducting electrodes via superconductor-insulator-normal metal (SIN) tunnel junctions [1; 2]. The incoming RF-signal increases the temperature of electrons in the normal metal, however in the presence of DC current hot electrons leave the absorber via SIN-junctions. An effect of electron cooling by the bias current increases the dynamic range of CEBs and reduces their noise equivalent power (NEP) [3; 4; 5]. Currently, the NEP level of CEBs is shown to be close to the photon noise limit [6; 7], which makes CEBs attractive for balloon-borne radio astronomical missions such as the Large Scale Polarization explorer (LSPE) [8] and OLIMPO [9]. When CEBs are arranged into a periodic 2D array acting as a frequency selective surface (FSS) the incident RF signal is evenly distributed between all the bolometers. The resulting saturation RF-power of the array pixel is higher than that of a single bolometer by the factor which approximately equals the number of bolometers in the pixel. For achieving optimal NEP with reasonably high responsivity, the low-frequency impedance of the array should be matched to the impedance of the readout amplifiers. This can be done by an appropriate combination of parallel and series DC-connections between elementary unit cells [10]. It was shown in [11; 12; 13] that bolometric arrays consisting of the half-wavelength unit cells and fabricated on thick substrates provided 5-10% bandwidth. A reasonable agreement was obtained [12; 13] between the experimentally measured frequency response and the one numerically calculated for the unit cell. In this paper we present the concept of an ultra-wideband array of CEBs with the unit cell size of the order of \(\lambda/10\) at the central frequency 350 GHz.
# Local Community Detection in Dynamic Graphs Using Personalized Centrality +
## 4 Communities from Personalized Centrality  
### Local Communities from Personalized Centrality  
Given a seed set of vertices of interest, we can calculate the personalized Katz or PageRank scores as \(\epsilon_{Katz}=A(I-\alpha A)^{-1}\mathbf{b}_{Katz}\) or \(\epsilon_{PR}=M_{PR}^{-1}\mathbf{b}_{PR}\), respectively, where \(M_{Katz}=I-\alpha A\) and \(M_{PR}=I-\alpha A^{T}D^{-1}\) with \(\mathbf{b}_{Katz}\) and \(\mathbf{b}_{Katz}\) are the corresponding right-hand sides as discussed in Section 2.3. If we want the personalized scores w.r.t. vertex \(i\), then \(\mathbf{b}_{Katz}=\mathbf{b}_{PR}=\mathbf{e}_{i}\). Intuitively, the resultant scores from a personalized centrality metric with respect to vertex \(i\) answers the question of how likely we are to reach vertex \(i\) from the rest of the graph. For the question of local community detection, this can be translated into how likely vertices in the graph are to belong to the community of vertex \(i\). For a community of size \(R\), we therefore take the top \(R\) vertices as ranked by the personalized centrality vector \(\epsilon_{PR}\) or \(\epsilon_{Katz}\) as the local community.  
Once personalized Katz or PageRank centrality scores are computed, the local community is then formed from those vertices with highest centrality values. Sorting the entire length \(n\) vector to obtain these top entries is too computationally expensive, especially in the dynamic setting where updated results are needed quickly after changes occur. Therefore, we extract the vertices with top \(k\) values using a heap. For the first \(k\) vertices, the centrality values are added to a heap. Thereafter, each centrality score is compared to the minimum value in the heap in \(\mathcal{O}(1)\) time and if larger, the minimum value is removed from and the new value inserted into the heap in \(\mathcal{O}(\log k)\) time. In the worst case, the centrality values are in ascending order and all such checks result in a removal and insertion, leading to a running time of \(\mathcal{O}(n\log k)\). However, experiments on real graphs show far fewer replacements.
## 1 Introduction
A new multifunctional support system for managing transport fleets called TARGET (Tracking Adaptation in Routing Guide Optimization System for Effective Transport) was proposed. It was motivated by the dynamic development of location and sensing technologies, information systems, and metaheuristic optimization algorithms. In its concept, the TARGET system assumes the implementation of many new or improved functionalities, such as dynamic route recalculation and optimization, supply chain planning, resource monitoring, load and order forecasting, and dynamic destination lists (so-called agile WFM). During the conducted research, it was found that the dynamically changing location of delivery points may significantly affect the decisions made by the optimization algorithm. It requires looking for solutions that guarantee quick updating of actual data, using solutions in location and routing optimization services. This article focuses on the issues of dynamic and quick recalculation of the value of the so-called distance matrix [29; 30], one of the most key data structures used in planning and optimization algorithms (further denoted as DistMx). Regardless of the applied problem, this matrix must present the current distances between the objects, generating from their current position. The proposed solution is also dedicated to the dynamic VRP variant, in which the set of current delivery points (destinations) may change frequently. The Authors pointed out that a proper updating of the distance matrix may be a key factor affecting the effectiveness of the optimization algorithms. Moreover, in evaluating the effectiveness of the optimization algorithm, the influence of the current values of the distance matrix is rarely examined. This aspect needs to be discussed in more detail.
## 6 Simulations and Results
### Evaluation of a Single Terminal  
Within the whole network, the devices and terminals work independently without cooperation under frequency division multiple access (FDMA), since the HAPs and the satellite have sufficiently covered the whole disaster area. Cooperation between the devices and terminals will lead to an undesirable complicated electromagnetic environment with additional interference. In this scenario, evaluation of an \(i\)-th terminal can be used to infer the energy efficiency of transmissions across all terminals, to a certain extent. Since the simulation of one terminal can be performed relatively easily, we evaluate the performance of the energy-efficient transmission strategies at a single terminal as a first step. We assumethat four HAPs are deployed and that the \(i\)-th terminal is randomly located. Table 2 lists the elements of the parameters used in simulations and their values. The Rician factors of channels \(h_{1}\) and \(h_{2}\) are \(\upkappa_{1}=12\;\mathrm{dB}\) and \(\upkappa_{2}=7\;\mathrm{dB}\). The thermal noise power is equal to \(-207\;\mathrm{dB}\). The back-off of the output power for the HAPs and satellite is \([BO_{o}]=3\;\mathrm{dB}\). During the periodic LSA broadcast, each HAP and satellite broadcasts its address (32 bits), geographic position (longitude \(\uppsi\) and latitude \(\upphi\), 64 bits), power \(P_{s}\) (32 bits) and antenna gain \(G_{s}\) (32 bits) to all users. A total simulation time of 120 s is assumed in this subsection. The \(i\)-th terminal, _i.e._, \(T_{i}\), sends 772 Kbits of data every \(\Delta t=500\;\mathrm{ms}\); the packet size of the LSA is 160 bits, and the total time taken for the LSA transmission \(T_{LSA}\) is 100 ms. The transmitted power calculated by the direct and relay links _versus_ the simulation time before applying the energy-efficient path selection is shown in Figure 2.  
Figure 2 shows the transmission power calculated by direct and relay links _versus_ runtime, before applying the energy-efficient path selection. In Figure 2, \(T_{i}\to Sat.\) denotes that the terminal only selects the T-S direct link. \(T_{i}\to HAP_{1}\to Sat.\), \(T_{i}\to HAP_{2}\to Sat.\), \(T_{i}\to HAP_{3}\to Sat.\) and \(T_{i}\to HAP_{4}\to Sat.\) denote that the terminal only selects the first HAP, the second HAP, the third HAP and the fourth HAP as a relay, respectively. For the T-S direct link, the transmission power is \(P_{s,1}(t)\), and for the T-H-S path, the transmission power is \(P_{r}(t)=P_{s,2}(t)+P_{s,3}(t)\). From Figure 2, we can observe that \(P_{r}(t)\) is lower than \(P_{s,1}(t)\) for most of the time slots within the runtime. Therefore, there is an opportunity to reduce the overall transmission power through adaptive path selection. Furthermore, we can also infer that the overall average transmission power of the optimized transmission path will be lower than that of all five paths in Figure 2.
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 2 Methodology
### Comparison of Application for Drive Test  
To evaluate mobile network performance at a particular location, performing a drive test is one of the preferable methods. To perform a drive test, a smartphone with an installed drive-test application is required to measure and record required data such as \(RSRP\), \(RSRQ\), data rates, latency, and a few other parameters.  
Plenty of applications are available on Google Play Store for Android devices to perform the drive test, such as GNet Track Pro, RF Signal Tracker, Network Cell Info, and ASCOM Terms. To choose a precise application, a field test was conducted at the Kilim Geoforest Park, Langkawi, Malaysia. The location is suitable for representing the worst-case scenario for a mobile network service. Among the available applications in the market, two applications were examined for the actual evaluation: GNet Track Pro [40] and Network Cell info [41].  
Figure 7 depicts the drive test results, which were measured via Gnet Track Pro and Network Cell info applications. Although both applications can measure and record required data for network performance evaluation, as shown in Figure 7 there are many blank paths in the results of the Network Cell info application, which means plenty of data lost during the drive test. In contrast, the Gnet Track application did not encounter the problem, as there are no gaps generated or blank plots along the route and it can record and store data correctly. Besides, the application also supports a drive test for two cellular networks at one time. This advantage will make it easier to work for actual drive tests.  
Figure 6: Mapped routes at the National University of Malaysia (UKM).  
Figure 7: Drive test results by Network Cell Info (**a**) and GNet Track Pro (**b**).  
Based on this result, the G-Net Track Pro has been selected to perform a cellular network drive test for the rest of the research work. G-Net Track Pro is a tool to perform drive tests and monitor cellular network performance. This application provides a wide range of features, such as cell measurement for serving and neighbor cells in both indoor and outdoor scenarios, measurement recording, map visualization, voice, and data test sequence for uplink and downlink, and cell scanning. The authors in [42,43] utilized G-Net Track Pro to study the LTE network performance in indoor and outdoor scenarios. The results showed that G-Net Track Pro is capable of providing accurate and reliable measurement results.
# Al-Foam Compression Tests in Parallel and Serial Concepts
## 3 Tools for Test  
The compression tests occurred on an Instron 8801 servo-hydraulic material testing device at the University of Debrecen laboratory. The test machine is capable of both dynamic and quasi-static compression, while its software records the displacement in mm and the effort in kN. An Instron 8800 MiniTower Control Unit was used to control the Istron 8801 material testing system. The software required to use the system is the WaveMatrix software package. The purpose of the measurement was to record the response properties of the metal foam to its compaction, i.e., to record the engineering stress-strain diagram and to evaluate it. The amount of data in the measurements is influenced by the sampling frequency. In this case,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Designation** & **Diameter** & **Height** & **Volume** & **Mass** & **Density** & **Rel. Density** \\ \hline SP-LD1 & 29.76 & 28.73 & 19.97 & 4.74 & 0.23 & 0.085 \\ \hline SP-LD2 & 29.55 & 28.95 & 19.84 & 4.54 & 0.22 & 0.081 \\ \hline SP-LD3 & 29.13 & 29.35 & 19.55 & 4.69 & 0.23 & 0.085 \\ \hline SP-LD4 & 29.88 & 29.81 & 20.89 & 4.81 & 0.23 & 0.085 \\ \hline SP-HD1 & 29.91 & 29.81 & 20.93 & 7.58 & 0.36 & 0.13 \\ \hline SP-HD2 & 29.95 & 28.73 & 20.23 & 7.02 & 0.34 & 0.12 \\ \hline SP-HD3 & 29.34 & 29.85 & 20.17 & 7.44 & 0.36 & 0.13 \\ \hline SP-HD4 & 29.79 & 29.34 & 20.43 & 7.49 & 0.36 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specim parameters. (Own collaboration of authors).  
Figure 2: Scheme of the investigations (**(a)** single compressions (**b**) parallel compression, (**c**) serial conceptions) (own collaboration of authors).  
the frequency was chosen to be 500 1/s, which resulted in more than 10,000 data by the end of the test. This frequency set-up was based on a recent measuring foam compression test experience. Over this frequency limit, the data were mishandled inaccurately at the end of the densification range by the system. Given the use of the quasi-static method, the compression speed was chosen to be 1 mm/s, and the crumpling displacement of 25/55 mm resulted in more than 80% of strain in the foam specimens. Knowing the acting force and the displacement during the compression, the stress-strain curve could be structured. For visualization and the interpretation of the results, the 3.16.2 version of the Logger Pro software was used. The aim of the study is to compare and reveal the effect of the foams of different densities in one method; therefore, the test environment and temperature conditions were the same for all tests. No additional materials--for example, lubricator--were used since they could have influenced the final results.
## 3 Dataset Creation of the Rock Tunnel Construction
### Deformation Computing Method  
Three sensor nodes are deployed on the primary tunnel lining in a typical transverse section near to the tunnel face (as shown in Figure 1a). The \(Y\)-axis of the sensor's built-in coordinate system is the same as the laser irradiation direction. The tilt angle, \(\beta\), between the \(Y\)-axis and the transverse sectional plane is used to compute the crown settlement and horizontal convergence. The measured laser distance is the real-time distance between the targeting point and the sensor. For the measurements at time \(i\), \(d_{i}\) is the laser distance and \(\beta_{i}\) is the \(Y\)-axis tilt angle. The computed values are \(S_{i}\) (crown settlement) and \(C_{i}\) (horizontal convergence), which can be derived as follows:  
\[S_{i}=d_{i}\sin\beta_{i}-d_{0}\sin\beta_{0} \tag{1}\]  
\[C_{i}=d_{i}\cos\beta_{i}-d_{0}\cos\beta_{0} \tag{2}\]  
where the subscript \(0\) denotes the initial moment for time \(i\), a negative \(S\) value indicates the tunnel crown displacing in the direction of gravity, and a negative \(C\) value indicates the tunnel lining converging horizontally.  
The above computation assumes that the sensors implemented for monitoring are rigidly attached to the tunnel lining to reflect the structural deformation. However, in an actual rock tunnel site during the construction process, the harsh environment near the rock tunnel face is complex. The sensor position and the laser beam are heavily influenced by the impact of the excavation blasting and other construction activities. Those unfavourable conditions can affect the tilt angle and laser distance measurements.  
From the site experience, the following scenarios are often encountered while monitoring the rock tunnel construction. Suppose the distance measurement suddenly decreases but returns to an expected value after a short period. In that case, construction personnel or equipment can consider a temporary laser beam blockage. Thus, those monitoring data can be eliminated if the distance data continue to be abnormal. Checking the sensor in person  
Figure 4: Three axes of the WTDS.  
and verifying if dust is blocking the laser sensor or some obstacles in front of the sensor is advised. If the tilt angle of any axis has a significant change at a particular time point, it can be considered as a movement of the sensor, which leads to a significant difference in the measured value. Other scenarios can also significantly affect the measured values. The laser lens can also be blocked by heavy dust and present invalid sampling values or abnormally short-distance values due to the drill and blasting construction activities.  
Although the adopted protective cover can mitigate some of those problems, the monitoring data quality is highly influenced by the construction activities. Therefore, it is advised that the site engineers should regularly check the sensor positions and clean the laser lens. In addition, the computed tunnel deformations should be periodically compared with manual inspection and another monitoring method to ensure the reliability of the measurements.
# A Survey on Dynamic Corrective Control of Asynchronous Sequential Machines
## 3 Stable Reachability and Detectability
### Detectability and Stable Reachability of Input/Output ASMs  
In contrast to input/state ASMs, direct access to the state is impossible in input/output ASMs and the output feedback has the form of a burst. Thus one must take into account an additional condition for representing stable reachability, termed detectability. As shown below, detectability specifies whether one can determine the end of a stable transition only by referring to the output burst [29; 30; 31].  
To describe detectability of an input/output ASM \(\Sigma\), define the following notation associated with \(\beta(x^{0},a)\) (see (1) and (2)).  
\[\beta_{-1}(x^{0},a):=\begin{cases}\gamma(h(x^{0})h(x^{1})\cdots h(x^{k-1}))&k \geq\ 1\\ \emptyset&k=0\end{cases}\]\(\Sigma\) is said to be detectable at \((x^{0},a)\) if  
\[\beta_{-1}(x^{0},a)\
eq\beta(x^{0},a).\]  
To interpret the meaning of the above relation, assume on the contrary that \(\beta_{-1}(x^{0},a)=\beta(x^{0},a)\). Then, at the moment \(C\) in Figure 1b receives the output feedback \(\beta_{-1}(x^{0},a)\), it is faced with ambiguity, namely, \(\Sigma\) may reach the next stable state \(s(x^{0},a)=x^{k}\), or it may be in the middle of the current stable transition, passing through the last transient state \(x^{k-1}\). Note that one must consider only detectable transitions in depicting stable reachability since otherwise \(\Sigma_{c}\) could not preserve fundamental mode operations. Clearly, all input/state ASMs are detectable trivially. Including detectability into the chain of stable transitions, one can describe the stable reachability of input/output ASMs in a similar way to Definition 1.  
**Definition 2**.: _Given \(\Sigma=(A,Y,X,\bar{x},f,h)\), \(R(\Sigma)\in(P(A^{+}))^{n\times n}\) is the matrix of stable transitions of which \((i,j)\) entry is defined as (\(i,j\in\{1,\ldots,n\}\))_  
\[R_{i,j}(\Sigma):= \{t\in A^{+}|s(x_{i},t)=x_{j},1\leq|t|\leq n-1\text{ and }\] \[\Sigma\text{ is detectable at all the intermediate stable pairs}\}.\]  
\(K(\Sigma)\in\{0,1\}^{n\times n}\) is the skeleton matrix of \(\Sigma\) of which \((i,j)\) entry is defined as  
\[K_{i,j}(\Sigma):=\begin{cases}1&R_{i,j}(\Sigma)\
eq\emptyset\\ 0&R_{i,j}(\Sigma)=\emptyset\end{cases}\]  
The studies of [29; 30; 31] lay a theoretical foundation on detectability and stable reachability of input/output ASMs. Polynomial algorithms for deriving the matrices in Definition 2 are also found in [29; 30; 31]. As in the case of input/state ASMs, there exist STP-based approaches [44; 45] in which the stable reachability of input/output ASMs is described in terms of Boolean matrices called the transition structure matrix (TSM) and the stable transition structure matrix (STSM).