esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Thailand is not known to be an earthquake-prone country; however, in 2014, an unexpected moderate earthquake caused severe damage to infrastructure and resulted in public panic. This event caught public attention and raised awareness of national seismic disaster management. However, the expertise and primary data required for implementation of seismic disaster management are insufficient, including data on soil character which are used in amplification analyses for further ground motion prediction evaluations. Therefore, in this study, soil characterization was performed to understand the seismic responses of soil rigidity. The final output is presented in a seismic microzoning map. A geomorphology map was selected as the base map for the analysis. The geomorphology units were assigned with a time-averaged shear wave velocity of 30 m (V\({}_{\text{S30}}\)), which was collected by the spatial autocorrelation (SPAC) method of microtremor array measurements. The V\({}_{\text{S30}}\) values were obtained from the phase velocity of the Rayleigh wave corresponding to a 40 m wavelength (C(40)). From the point feature, the V\({}_{\text{S30}}\) values were transformed into polygonal features based on the geomorphological characteristics. Additionally, the automated geomorphology classification was explored in this study. Then, the seismic microzones were compared with the locations of major damage from the 2014 records for validation. The results from this study include geomorphological classification and seismic microzoning. The results suggest that the geomorphology units obtained from a pixel-based classification can be recommended for use in seismic microzoning. For seismic microzoning, the results show mainly stiff soil and soft rocks in the study area, and these geomorphological units have relatively high amplifications. The results of this study provide a valuable base map for further disaster management analyses.  
microzoning map; geomorphology classification; time-averaged shear wave velocity at 30 m (V\({}_{\text{S30}}\)); soil characteristics +
Footnote †: journal: Geo-Inform
# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
## 3 The Proposed Predictive Handover Management Approach
### Predictive Handover Procedure
AMF and UPF in the 5G architecture refer to access and mobility management function (AMF) and user plane function (UPF).
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 4 Space Radiation Effects
### Mach-Zehnder Modulators  
A Mach-Zehnder modulator (MZM) is an electrical-to-optical (E/O) converter topology commonly used in photonic transmitters. It utilizes the plasma dispersion effect to alter the phase of the optical signal [82], and a two-arm topology to convert phase modulation to amplitude modulation via constructive and destructive interference [83]. The understanding of the various forms of radiation effects is still in its infancy for MZMs. To date, several TID and DD studies have been conducted, but, to the authors' best knowledge, no studies with SEEs exist in the literature.
## 8 Conclusion and Future Work  
In this paper we presented our design and implementation of a service-oriented framework for multimedia content adaptation. Furthermore, we conducted extensive experiments within our framework. In contrast to common belief, we were able to show that content adaptation under soft real-time constraints is possible in a service-oriented way. Moreover, Web services allow the reuse of adaptation components in different personalization workflows. One remaining problem is the lack of support for continuous data streams in current Web service infrastructures. Our framework uses a second protocol layer to support the transport of continuous data between Web services.  
Figure 20: Service recovery time.  
Our experiments show that the overhead of network and IO operations is rather low compared to the actual content adaptation process (for instance around 200 ms as compared to 7 s in a four service workflow). It is doubtful whether in other currently available distributed approaches, namely P2P systems, usual peer devices with limited processing power are able to fulfill the computationally expensive personalization task at all. Still, in contrast to P2P systems with startup times in the range of minutes, our system was able to personalize and deliver multimedia content after only a few seconds. The actual startup time for an adaptation workflow, personalizing a 60 s movie using four services is 7 s. The overall delivery time--the time until the whole content object is available at the client--is 20 seconds. Even for more complex workflows, containing more personalization steps, the processing and delivery time scales well (_i.e._, an additional 3 s per task for the most expensive service type).  
As a direct effect of implementing the workflows as a pipeline of services, the processing time is largely limited by the performance of the slowest adaptation service in the workflow. To provide a continuous stream at the client side, timely replacement of failing services is needed. For this we implemented the PUMA E\({}^{2}\)Mon monitoring protocol [10]. Our evaluations show that the Monitoring Service we implemented does not hamper the speed of the adaptation process, but allows replacing failing services on-the-fly. Average times for replacing a failing service, from the detection of the failure until the re-establishment of the content stream, averages 3.8 s.  
Although the results presented here are already very promising, the problem of service-oriented multimedia applications is more difficult than our testbed shows. Future efforts will go into the development and evaluation of techniques for creation of workflows considering QoS constraints. Furthermore, we want to investigate the use of highly specialized tools for content adaptation to reduce the influence of the processing time as the limiting factor.
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 2 Methodology
Condition C0 is the obvious requirement that the synchronizer must not cause errors. Condition C1 ensures that the synchronization satisfies the _intention-confined effect_ (no surprise changes in the merged filesystem) requirement R1 as \(\mu\) should consist only of commands which were supplied by the replicas. The other consistency requirement R2 is guaranteed by C2 as \(\mu\) is maximal, therefore it preserves as much of the intention of the users as possible. Note that this definition of a synchronized state is never vacuous. There are only finitely many sequences which satisfy C0 and C1 (as every command in \(\mu\) comes from either \(\alpha\) or \(\beta\) and no repetitions are allowed), and there are at least two, namely \(\alpha\) and \(\beta\). Because of this, the empty sequence (undoing all changes) will not satisfy C2 (assuming one of \(\alpha\) and \(\beta\) is not empty), in line with our requirements. The declaration-based synchronization is intuitively clear, easy to understand, and does not use any predetermined conflict-resolving policy. An operational characterization is proved in Theorem 2. The essence of this theorem is that any merged filesystem \(\Psi\) can be generated from the replica, say \(\Phi_{1}\), by  
* rolling back some of the commands in \(\alpha\), followed by
* applying some commands from the other sequence \(\beta\),  
and vice versa for the other replica. The commands rolled back represent a minimal set whose removal resolves all conflicts. These commands also give users a clear understanding of the changes the synchronizer wants to perform on their filesystem. They are also helpful when some of the rolled-back commands should be introduced again after the merging (doing a redo of the undo). Turning to traditional conflict-based synchronization, Section 6 proves that any declaration-based merged state, and only those states, can be the result of a conflict-based synchronization algorithm. For each pair of canonical sequences describing the replicas to be synchronized we define what the conflicting command pairs are. Their resolution uses the winner/loser paradigm: the winner command is accepted while the loser command is discarded. It turns out that resolving a conflict may automatically resolve some of the subsequent conflicts, but no new conflicts are created. Using this result the outcomes of different conflict resolving policies can be investigated easily. In particular, the _iterative approach_[16] always works, which applies all non-conflicting commands, resolves, in any way, the first conflict (which might automatically resolve other existing conflicts), and iterates.
# Trigonometric Inference Providing Learning in Deep Neural Networks
## 2 Related Work
### Hardware Optimization for Hyperbolic Functions with CORDIC  
Hardware optimization for hyperbolic activation functions has been reported in previous studies. Although the chip area and power consumption are reduced, related studies apply hyperbolic functions only to activation computations, which limits the optimization gains. The CORDIC algorithm [1] is considered an efficient method for computing hyperbolic activations. Here, we briefly introduce the CORDIC algorithm, which is widely used in microcontrollers and FPGAs. Initially proposed in 1959, the CORDIC algorithm is an efficient way to calculate hyperbolic and trigonometric functions. A CORDIC module computes the trigonometric value of a certain angle through accumulative rotations of the preset base angles. It decomposes a certain angle into discrete base angles that are pre-stored in a look-up table. Note that all the base angles are chosen as powers of 2; thus, iterative rotations of an angle are computed through shift operations. The CORDIC algorithm then iteratively applies accumulative rotations of the base angles until the required precision is met.  
Many accelerated CORDIC algorithms have been proposed over the years. For example, [PERSON] et al. proposed a scale-free CORDIC engine that can reduce the chip area and power consumption by 36% [19]. [PERSON] et al. proposed a sine and cosine CORDIC compute engine that requires only seven iterations for 16-bit precision [20]. An improved rotation strategy was also studied in [21], which achieved 32 precision with only approximately five iterations on average.  
[PERSON] et al. used a CORDIC engine to compute an application-specific algorithm [3], i.e., an independent component analysis (ICA), which is used for the direct separation of a number of mixed signals. In their research, only a part of the updating scalar \(\tanh(x)\) was computed using the CORDIC engine. The method still relies on a comparatively large number of multipliers. A similar approach was adopted by [PERSON] et al., who used CORDIC to compute activations in neural networks [22].  
Another direction to take advantage of hyperbolic functions is by applying it to specific computation tasks. [PERSON] et al. utilize the CORDIC engine to compute modified Izhikevich neurons [23]. Similarly, [PERSON] et al. realized an efficient implementation of a cerebellar Purkinje model [24].  
To the best of our knowledge, there is still a lack of training algorithms that can remove the usage of multipliers on deep neural networks during the training stage.
# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 2 Trends in Malware Detection
### Shortcomings of Conventional Machine Learning Based Methods for Detecting Malware  
In the case of static analysis being used by researchers, the foremost problem which hinders the analysis process is obfuscation, encryption, and packing. Refs. [34, 35, 68, 69, 70, 71] have executed the solution without catering to the issue of obfuscation, packing, and encryption. One of the major problems seen in many papers during the survey is the problem of anti-analysis techniques which can be called evasion techniques also. Professional malware developers or in other words sophisticated malware developers take care of the fact that the target machine can be an analysis machine or can have a virtual environment setup, so they purposefully make use of evading techniques through which, normally, first they check for the presence of virtual environment and in case of its presence malware hibernates itself. This is called environmental awareness and is very clearly stated in [58]. Malware can easily comprehend and identify if it is being run in a virtual or debugging environment. Another evasion approach is timing-based which means malware gets only active at any date or time or gets activated at user interaction only. In solutions applied by [32, 72, 73, 74] detection accuracy gets noticeably reduced on facing the evasion techniques, encrypted malware, and if malware needs user interaction for getting activated. Another problem that was identified during the survey was the small or insufficient datasets being used for analysis due to which results produced might not be reliable.  
Researchers in [28, 30, 32, 37, 38, 68, 69, 70, 71, 72, 74, 75, 76, 77, 78, 79] used small dataset. Since conventional machine learning algorithms are supposed to carry out the process of feature engineering, therefore, a very prominent problem that could be seen during the paper survey related to machine learning-based solutions for malware detection was the use of few features out of all those features which can very distinctively play a vital role in the detection of malware. Solutions carried through in [30, 36, 38, 80, 81] considered only a subset of useful features. Another shortcoming was the lack of capability of detecting the variants of malware.
# Comparison of Condition Rating Systems for Bridges in Three European Countries
## 3 Slovakia
### Slovak Condition Rating System for Road Brides  
The Slovak condition rating system issued in 2013 is based on the classification of the bridge in question into one of the seven possible condition classes (from I. to VII.). The description of the condition classes is listed in Table 4. The purpose of the Slovak methodology is to guide the management of bridges and to introduce a unified system of records, supervision, preparation of maintenance, repairs and reconstruction of bridges and their implementation [16]. In general, condition classes I. to III. describe the structural state which is good and without significant risk in the near future. Condition class IV. represents the state of bridges with possible risks concerning their structural condition in the near future, but which are currently usable structures. However, condition classes from V. to VIII. define a state with the required repair or even immediate closure of the bridge for traffic. Due to the scope of the paper, only the basic principles of bridge evaluations according to the Slovak guidelines are listed.  
Figure 2: Distribution of Italian roadways and infrastructures: (**a**) ANAS highways and roadways [10]; (**b**) bridge locations for each region [13].  
The bridge management system is supposed to provide, based on processed and evaluated information, data related to the condition of bridges and documents for planning repairs and reconstructions.  
In general, the following three aspects are decisive for decision-making [16; 17]:  
* The structural state which reflects the difference between the real technical parameters of the structure and designed parameters;
* An operational capability which represents the difference in operational parameters of the bridge (such as load-carrying capacity, traffic intensity, passing speed, passing profile, etc.) compared to the parameters required for the road on the bridge and load-carrying class of the bridge;
* Impact on the environment.  
Condition rating systems [16] in Slovakia can be divided into two groups:  
* Condition rating systems based on index values, which are determined using mathematical-statistical operations with ratings of structural elements and classification of their failures;
* Condition rating systems based on probabilistic calculations of structural reliability with regard to their load-carrying capacity and service life.
## 2 Materials and Methods  
### General Procedures  
Optical rotations were measured on a Perkin Elmer 241 automatic polarimeter in CHCl\({}_{3}\) at 25 \({}^{\circ}\)C, and the [\(\alpha\)]\({}_{\rm D}\) values are given in 10\({}^{-1}\) deg cm\({}^{2}\) g\({}^{-1}\). UV spectra were obtained in absolute EtOH on a JASCO V-560 instrument. IR (film) spectra were measured in CHCl\({}_{3}\) on a Bruker IFS 55 spectrophotometer. \({}^{1}\)H (400 or 500 MHz) and \({}^{13}\)C (100 or 125 MHz) NMR spectra were recorded on Bruker Avance 400 or 500 spectrometers; chemical shifts are given in ppm and coupling constants in hertz. Samples were dissolved (CDCl\({}_{3}\): \(\delta\)\({}_{\rm H}\) 7.26, \(\delta\)\({}_{\rm C}\) 77.0). EI-MS and EI-HRMS were recorded on a Micromass Autospec spectrometer. Silica gel 60 (particle size 15-40 \(\upmu\)m) for column chromatography and silica gel 60 F254 for analytical (TLC) and preparative thin-layer chromatography (PTLC) were purchased from Macherey-Nagel. Sephadex LH-20 was obtained from Pharmacia Biotech. Shimadzu high-performance liquid chromatography (HPLC) equipment consisted of a pump LKB 2248 solvent delivery module, SPD-6V detector set at 254 nm, using a semipreparative silica gel column (Waters \(\upmu\)-Porosil(\(\upmu\)), 15 cm \(\times\) 1.6 mm, particle size 10 \(\upmu\)m). The mobile phase consisted of a mixture of \(n\)-hexane-EtOAc (8:2) in isocratic mode with a flow rate of 9 mL/min. The degree of purity of the compounds was over 95%, as indicated by a single peak in HPLC and NMR. All solvents used were of analytical grade (Panneac), and the reagents, used without purification, were purchased from Sigma-Aldrich. Pristimerin, used as starting material, was isolated from the root bark of _M. blephardes_ and _M. canariensis_, as previously described [20; 21].
# Smooth Design of 3D Self-Supporting Topologies Using Additive Manufacturing Filter and SEMDOT
## 3 Numerical Experiments
### Different Build Orientations  
A deep cantilever beam shown in Figure 2 is considered to demonstrate the influences of different build orientations on compliance, the number of iterations and topologies. In AM, the build orientation is the accumulating orientation of materials when building the part [41]. As illustrated in Figure 2, the left end is fixed and a unit vertical load (\(F_{y}\) = 1 N--Figure 2) is imposed in the middle of the lower edge on the free end. In this study, three build orientations--\(x_{\text{max}}\), \(y_{\text{min}}\) and \(z_{\text{max}}\)--are considered (see Figure 2). The design domain is discretized by a 60 \(\times\) 40 \(\times\) 30 finite element mesh.  
Figure 3a shows the reference design case, which converges at 5.226 J after 88 iterations; the optimized topology with the build orientation \(x_{\text{max}}\) converges at 5.292 J after 201 iterations; the optimized topology with the build orientation \(y_{\text{min}}\) converges at 5.374 J after 165 iterations; and the optimized topology with the build orientation \(z_{\text{max}}\) converges at 5.378 J after 139 iterations. It is noted that the reference case is the topological design obtained without using [PERSON]'s AM filter (i.e., general topology optimization), and J (N-mm) denotes the multiplication of the force (N) by displacement (mm). Performance sacrifices of 1.25 %, 2.83 %, and 2.91 % are made to form self-supporting designs for build orientations \(x_{\text{max}}\), \(y_{\text{min}}\) and \(z_{\text{max}}\), respectively. Even though the self-supporting design in the build orientation \(y_{\text{min}}\) (Figure 3d) is the most similar to the reference design (Figure 3b), the self-supporting design in the build orientation \(x_{\text{max}}\) (Figure 3c) is the closest to the ref  
Figure 2: Design domain of deep cantilever beam.  
erence design in terms of performance. The self-supporting design in the build orientation \(z_{\text{max}}\) (Figure 3e) obtains the worst performance, as great changes have to be made for this specific direction.  
Figure 3: Convergence processes and optimized topologies with different build orientations: (**a**) convergence processes, (**b**) reference, (**c**) \(x_{\text{max}}\), (**d**) \(y_{\text{min}}\), (**e**) \(z_{\text{max}}\).
## 4 Discussion
### Analysis of the Evaluated Metrics for Each Method
#### 4.1.5 Stability Index  
The stability index, defined as the ratio between residual variance and the cycles used, shows that V2 GGlide performs better at high SoC levels (>80%). However, V2 GSmooth maintains more consistent values across all SoC levels, making it ideal for dynamically changing operating conditions where stability must be ensured regardless of storage availability.
## 3 Data and Method
### RS Images Processes
#### 3.1.2 Classification Methodology  
After a series of image processing steps, human-computer interactive interpretation under overall digital environment was conducted to construct the LULC datasets with the field survey data, vegetation map, topographic map and grassland map as auxiliary data. The LULC map of 2004 was constructed first.  
To guarantee the classification accuracy and efficiency, we performed the interpretation for the 1970s, 1990s and 2012 RS images by extracting the change information directly from the comparison with the RS images in 2004. After the fusion of the change information for the period of the late 1970s-2004, the early 1990s-2004 and 2004-2012 and the LULC map of 2004, the LULC maps of the late 1970s, the early 1990s and 2012 were eventually constructed. However, the spatial resolution of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Chinese LULC**} & \\
**Types in this Study** & **Classification System** & **Description** \\  & [28; 29] & & \\ \hline low-coverage grassland & 33 low-coverage grassland & Grassland with the coverage between 5\% and 20\% \\ \hline \multirow{3}{*}{wet- and marshland} & Water bodies & 41 stream and rivers 42 lake & \\  & 43 reservoir and ponds & Natural water bodies, wetland, and \\  & permanent ice and snow & 44 permanent ice and snow & land for water conservancy facilities \\ \cline{2-4}  & shoaly land and swamp & 46 shoaly land 64 swamp & \\ \hline \multirow{3}{*}{build-up area} & 51 urban land 52 rural & Residential, commercial and services, \\  & residential area 53 other & industrial, transportation \\ \cline{1-1}  & construction land & & \\ \hline \multirow{3}{*}{sand, gobi and bare land} & 61 sand land 62 gobi 63 & Land covered with sand, gravel or soil \\ \cline{1-1}  & saline-alkali soil 65 bare & with the vegetation coverage lower \\ \cline{1-1}  & land 66 bare rock & than 5\%, or land covered with \\ \cline{1-1}  & desert & 67 other unused land & alpine desert, tundra, _etc._ \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._MSS was 80 m, which can cause low classification accuracy [6]. Under this consideration, we set the topographic map with a scale of 1:100,000 produced in the early 1980s as another information source, which was an effective supplement for the interpretation.  
To improve the classification accuracy, information from the grassland map, the vegetation map and the topographic map was fully obtained; the horizontal zonality and vertical zonality of LULC distribution, the effect of topography on spatial distribution of LULC and change of main LULC types, especially grassland, glacier and water bodies, were all of interest as well.