esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design of Analytical Models
### Ground Motion and Scaling
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Analytical** & **No.** & **Earthquake Name** & **Station Name** & **M\({}_{w}\)** & **R\({}_{\text{jp}}\) (km)** & **V\({}_{\text{v,30}}\) (m/s)** \\ \hline \multirow{6}{*}{Model A} & 1 & Imperial Valley-06 & Delta & 6.53 & 22.0 & 242 \\  & 2 & Northridge-01 & Northridge-17645 Saticy St & 6.69 & 0.0 & 281 \\  & 3 & Superstition Hills-02 & Brawley Airport & 6.54 & 17.0 & 209 \\  & 4 & Northridge-01 & Playa Del Rey-Saran & 6.69 & 24.4 & 346 \\  & 5 & Loma Prieta & Fremont-Emerson Court & 6.93 & 39.7 & 285 \\  & 6 & Kobe Japan & Sakai & 6.90 & 28.1 & 256 \\  & 7 & Northridge-01 & Camarillo & 6.69 & 34.8 & 351 \\ \hline \multirow{6}{*}{Model B} & 1 & Imperial Valley-06 & Delta & 6.53 & 22.0 & 242 \\  & 2 & Northridge-01 & LA-Century City CC North & 6.69 & 15.5 & 278 \\  & 3 & Chuetsu-oki Japan & Niigata Nishi Kaba District & 6.80 & 27.8 & 255 \\  & 4 & Loma Prieta & Dumbarton Bridge West End FF & 6.93 & 35.3 & 238 \\  & 5 & Chuetsu-oki Japan & Kashiwazaki NPP Unit 1: Ground surface & 6.80 & 0.0 & 329 \\  & 6 & Chi-Chi Taiwan & CHY036 & 6.30 & 45.1 & 233 \\  & 7 & Chuetsu-oki Japan & Hinodecho Yoshida Tsubaue City & 6.80 & 20.4 & 262 \\ \hline \multirow{6}{*}{Model C} & 1 & Imperial Valley-06 & Delta & 6.53 & 22.0 & 242 \\  & 2 & Northridge-01 & LA-Century City CC North & 6.69 & 15.5 & 278 \\  & 3 & Chi-Chi Taiwan-06 & CHY036 & 6.30 & 45.1 & 233 \\ \cline{1-1}  & 4 & Northridge-01 & Playa Del Rey-Saran & 6.69 & 24.4 & 346 \\ \cline{1-1}  & 5 & Northridge-01 & LA-Pico \& Sentous & 6.69 & 27.8 & 305 \\ \cline{1-1}  & 6 & Loma Prieta & APEEL 7-Pulgas & 6.93 & 41.7 & 415 \\ \cline{1-1}  & 7 & Chuetsu-oki Japan & Joetsu Kita & 6.80 & 29.0 & 334 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of ground motion used in seismic design. Figure 3: Target response spectra and average response spectra by seven pairs of ground motion scaling (Site class: \(S_{4}\), maximum considered earthquake (MCE), Model B).
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 2 Proposed Model
### Energy Function Computation  
The energy function defines the energy consumed by nodes during the data-transmission process. Road maintenance and neighbor-sensing are the main responsibilities of the energy model. It can be calculated as follows:  
\[F_{n,\rho}=[(qk_{n,\rho}\times qs_{n,\rho})+(qs_{n,\rho}\times rs_{n,\rho})+ (qr_{n,\rho}rs_{n,\rho})] \tag{5}\]  
Here, \(F_{n,\rho}\) shows the total energy function, \(qk_{n,\rho}\) shows the energy required by the \(k\)th node, \(qs_{n,\rho}\) defines the energy required for the \(s\)th node, \(qr_{n,\rho}\) defines ther energy required for \(r\)th node, and \(Vs_{n,\rho}\) shows the energy required for root identification. After the selection of a trustworthy node to transmit the packet, its energy dissemination should be updated for further transmission of the packets. The total energy (\(Eim,n\)) can be simplified as follows:  
\[Fk_{n,\rho}=Fk_{n,\rho}-F_{n,\rho} \tag{6}\]  
Here, \(Fk_{n,\rho}\) shows the energy function of the second iteration. \(F_{n,\rho}\) shows the energy function of the first iteration.  
The trust value (\(TC_{t,k}\)) can be estimated by using the computed trust, energies, and mobility values as follows:  
\[TC_{t,k}=F_{t,k}+TR_{i}-N_{i} \tag{7}\]Here, \(F_{t,k}\) shows the trust factor, \(k\) defines the secret key, \(TR_{i}\) shows the transmission distance of the \(i\)th node, and \(N_{i}\) represents the mobility of \(i\)th node.  
A node with the largest trust value is selected for the data transmission. The route discovery must be done once the destination is reached or the neighbor's estimation must be done again. The network's route between a source node and nearest neighboring nodes is evaluated using the proposed trust model. If the distance between the source and the nearest node falls within the coverage region, the current node's information is added to the list of neighboring nodes. The log report for the estimated neighbor node is obtained for further analyses.
# LightSeg: Local Spatial Perception Convolution for Real-Time Semantic Segmentation
## 3 Methods
### Local Spatial Perception Convolution  
This paper introduces a novel neural convolution architecture called \"Local Spatial Perception Convolution (LSPConv)\" that integrates local perception and long-range dependency capturing. Figure 2 illustrates the basic structure of LSPConv, highlighting its distinctive features compared to ordinary convolutions.  
The LSPConv module aims to extract more informative features from the input feature map by applying different types of convolutions to different parts of the input channels. Specifically, the input feature map is divided into two channel parts, \(f_{a}\) and \(f_{b}\), which are processed separately using different convolutional layers. The mathematical expression is shown in Equation (1). Figure 3 provides a more visual demonstration of this process.  
\[\begin{split} f_{a}^{out}&=W_{a}*f_{a}+b_{a}\\ f_{b}^{out}&=W_{b,1}*(W_{b,2}*f_{b})+b_{b}\\ f^{out}&=\sigma(\text{Concat}(f_{a}^{out},f_{b}^{ out}))\end{split} \tag{1}\]  
For \(f_{a}\), a standard convolutional layer with weight parameters \(W_{a}\) and bias parameters \(b_{a}\) is applied. This operation produces the output feature map \(f_{a}^{out}\).  
For \(f_{b}\), a depthwise separable convolutional layer is used, which consists of a depthwise convolutional layer. The depthwise convolution applies a separate filter to each input channel. In this case, the depthwise convolutional layer applied a filter represented by the weight parameters \(W_{b,1}\) and \(W_{b,2}\) to the input feature map \(f_{b}\), The output feature map of this operation is denoted as \(f_{b}^{out}\).  
Finally, the output feature maps \(f_{a}^{out}\) and \(f_{b}^{out}\) are concatenated along the channel axis to form the final output feature map \(f^{out}\) using the concatenation operator Concat.  
Figure 2: The figure illustrates the distinction between feature extraction methods: standard convolution, grouped convolution, and our proposed LSPConv module. In standard convolution, a small kernel size is applied uniformly across all channels for feature extraction. Grouped convolution divides the input channels into groups, performing feature extraction using standard convolution within each group. In contrast, our LSPConv module partitions the channels into two groups, utilizing standard convolution for one group and spatial separable convolution for the other group.
## 5 Experimental Setup and Database
### Baseline and RBM Vector Setup  
For all the experiments, 20 dimensional MFCC features were extracted, for both the baseline and proposed systems, using a Hamming window of 25 ms with 10 ms shift. A 512 component UBM was trained to extract i-vectors for the baseline system and the PLDA was trained with the background i-vectors. A more recent and competitive features could have been used, for example the BottleNeck Features (BNF). These features (either in the baseline or in the proposed approach) would require a huge amount of labeled background data (for example phonetic labels). On the other hand, MFCC features do not require labeled data for training our models. This is the strength of our proposed RBM vectors, which were trained in a completely unsupervised manner. The UBM training, i-vector extraction, i-vector testing and PLDA training were carried out using Alize, a free open source toolkit [44].  
For the proposed system, more than 3000 speaker segments were extracted from the background audios according to the ground truth rich transcription. For each segment, the features of 4 neighboring frames were concatenated in order to generate 80-dimensional feature inputs to the RBMs. With a shift of one frame, we generated almost 10 million samples for the URBM training. All the RBMs used in this paper consisted of 80 visible and 400 hidden units. The URBM was trained for 200 epochs with a learning rate of 0.0005, weight decay of 0.0002 and a batch size of 100. All the adapted RBM models for the segments and target speakers were trained with 200 epochs with a learning rate of 0.005, weight decay of 0.000002 and a batch size of 64.  
For the baseline i-vector system, the hyperparameters were set to the typical values that are commonly used in speaker recognition tasks. For the proposed RBM vector system, the set of hyperparameters, that is, the visible and hidden units in all the RBMs, the number of epochs and batch size for the URBM, and learning rate for the adapted RBM models were adopted from our previous work in Reference [22]. For the adapted RBM models, we used a higher value for the number of epochs and a slightly lower value for batch size because the segments were very short as compared to our previous work in Reference [22].  
The PCA was trained with the background RBM supervectors and was applied to the background RBM supervectors and test RBM supervectors, as discussed in Section 2.3. Finally, fixed dimensional RBM vectors were extracted for the test speakers that were used in the speaker tracking and clustering experiments. Different dimensions for the RBM vectors were evaluated in the experiments which is discussed in Section 6.
## 4 Case Study Investigation
### Asphalt Mixture Transportation Monitoring System  
The asphalt mixture transportation monitoring system installed the BDS positioning module on the mixture vehicle, tracked the vehicle's driving route, collected data such as transportation route and transportation time, and formed a historical track playback route map (as shown in Figure 15). Through radio frequency identification technology, the team collected the time when the mixture came out of the field and started to be transported, and the time when the transportation was finished and started to be paved. In addition, a range area (as shown in Figure 16) was set on the electronic map of the construction project. Once the transport vehicle went outside this area, the system automatically recorded the data and sounded an alarm for the operator to respond appropriately.  
Figure 14: Mixing construction site.  
Figure 13: Mixing monitoring report.
###### Abstract  
Device-to-device (D2D) communications can be adopted as a promising solution to attain high quality of service (QoS) for a network. However, D2D communications generates harmful interference when available resources are shared with traditional cellular users (CUs). In this paper, network architecture for the uplink resource management issue for D2D communications underlaying uplink cellular networks is proposed. We develop a fractional frequency reuse (FFR) technique to mitigate interference induced by D2D pairs (DPs) to CUs and mutual interference among DPs in a cell. Then, we formulate a sum throughput optimization problem to achieve the QoS requirements of the system. However, the computational complexity of the optimization problem is very high due to the exhaustive search for a global optimal solution. In order to reduce the complexity, we propose a greedy heuristic search algorithm for D2D communications so as to find a sub-optimal solution. Moreover, a binary power control scheme is proposed to enhance the system throughput by reducing overall interference. The performance of our proposed scheme is analyzed through extensive numerical analysis using Monte Carlo simulation. The results demonstrate that our proposed scheme provides significant improvement in system throughput with the lowest computational complexity.  
device-to-device communications; uplink resource management; fractional frequency reuse; interference; quality of service; greedy heuristic search algorithm; binary power control scheme +
Footnote †: journal:
# Structure-Property Relation of Trimethyl Ammonium Ionic Liquids for Battery Applications
## 3 Results and Discussion
### Viscosity
The combined effects of the altered cation conformation upon introduction of ether functionalities increases the fluidity of the ionic liquids drastically and gives an explanation why the viscosities of [N111(2O2)][TFSI] are even slightly lower than the ones with the shorter side chain, namely [N111(2O1)][TFSI]. Addition of lithium salts to ionic liquids raises their viscosities as a general finding [48] and is the result of the charge localization on the small, spherical lithium cation which leads to the formation of clusters with the anions [49]. The calculated \(\delta_{\eta}\) values are in the range for highly fragile glass-forming liquids as commonly found for ionic liquids [50]. A slight decrease for cyclic ammonium [FSI]\({}^{-}\) ionic liquids upon additions of [Li][FSI], similar to the findings for the aliphatic ammonium here, has also been reported [51]. Since the investigated set of ionic liquids is quite similar in the structure and shows a comparable \(T\)-dependence, the activation energies of the viscous flow show the same trends as the values of the viscosity.
## 4 Tether Simulation
### Experimental Results  
We created a simple 2D computer simulation to simulate the effects of moving one edge of the cable while the other end is pinned (Figure 9). Figure 11 shows the behaviour of the rope with and without damping with respect to time. The wave motion continues to propagate through the rope when there is no damping whereas with damping the wave energy slowly decays and random forces are becoming more dominant. In Figure 12 we can see our desired effect when using coefficient 0.9. The movement of the cable at one end does not affect the other end, so long as there is no tension in the cable segments.  
Figure 10: A 2D model of a flexible cable where one of the end points \(A\) is moved with a constant velocity \(v\) towards a target \(S\).  
Figure 9: Experimental verification of motion of an extensible cable. We colored the cable in a checkers like pattern to enable the extension of the cable to be observable.  
Figure 11: The damping effect. This figure shows the cable in different times. In \(t=0\) we start to move the edge of the cable in the direction up and right along the “xy” plane. The first figure shows the results without damping and the second shows the behaviour of that cable with damping coefficient of 0.98. We can clearly see that the damping is absorbing the wave energy as we would expect in aquatic systems.  
Figure 12: Damping with 0.9 coefficient. Tuning the coefficient to 0.9 causes the desired effect for underwater simulation in the case of a low tension cable in an underwater environment. Disturbance on one side remains local.  
Figure 13 shows the random forces effect. In this experiment we look at the cable configuration in the 2D space after the system is stabilized (\(t>>0\)) with and without random forces. We can see that the random forces create a kind of memory loss effect of the shape of the cable. This effect is important when there are no other significant forces (or they are close to zero) in the system. When we don't add the random force, the cable tends to stand still in contrast to what would be expected in a dynamic aquatic environment.
# The Effect of Chemical Activating Agent on the Properties of Activated Carbon from Sago Waste
###### Abstract  
The effect of chemical activators on the properties of activated carbon from sago waste was conducted in this study by using ZnCl\({}_{2}\), H\({}_{3}\)PO\({}_{4}\), KOH, and KMnO\({}_{4}\) chemical solutions. The carbonized sago waste was added to each chemical solution, boiled at 85 \({}^{\circ}\)C for 4 h, and heated at 600 \({}^{\circ}\)C for 3 h. The porosity, microstructural, proximate, and surface chemistry analyses were carried out using nitrogen adsorption with employing the Brunauer Emmett Teller (BET) method and the Barret-Joyner-Halenda (BJH) calculation, scanning electron microscopy by using energy dispersive spectroscopy, X-ray diffractometer, simultaneous thermogravimetric analysis system, and the Fourier-transform infrared spectroscopy. The results showed that the activated carbon prepared using ZnCl\({}_{2}\) acid had the highest specific surface area of 546.61 m\({}^{2}\)/g, while the KOH activating agent surpassed other chemicals in terms of a refined structure and morphology, with the lowest ash content of 10.90%. The surface chemistry study revealed that ZnCl\({}_{2}\) and KOH activated carbon showed phenol and carboxylate groups. Hence, ZnCl\({}_{2}\) acid was suggested as activating agents for micropore carbon, while KOH was favorable to producing a mesopore-activated carbon from sago waste.  
sago waste activated carbon; activating agents; acid treatment; base treatment; porous properties; structural properties; thermal decomposition; surface groups +
Footnote †: journal: Applied
###### Abstract  
Ultra-high-performance concrete (UHPC) is a kind of building material with ultra-high strength, toughness, and durability. However, under the conditions of ordinary molding technology, most of the fibers cannot play a bridging role in the direction of force. In this study, UHPC specimens with different steel fiber contents (0%, 2%, 4%, and 6% by volume) and directional reinforced fiber were prepared. Based on the split-Hopkinson pressure bar (SHPB), the influence of directional distributed steel fiber on the dynamic impact mechanical properties of the UHPC specimen were systematically investigated. The stress-strain curves, stress peaks, dynamic increase factor (DIF), and ductile energy absorption properties of the specimens at different strain rates were obtained. The results showed that oriented steel fiber significantly increases the dynamic property of UHPC. The dynamic impact peak strain, peak stress, and DIF of the UHPC specimen with 2% oriented steel fiber were 35.78%, 8.8%, and 12.6% higher than that prepared by normal molding technology, respectively. Moreover, with the increase of fiber content, the peak stress, energy absorption, and multiple-impact compression resistance of the specimen were greatly improved. When the fiber content was 6%, the dynamic impact peak strain, dynamic impact compressive strength ratio, and energy absorption capacity of the specimen were 3.09, 1.45, and 4.1 times the reference group, respectively.  
UHPC; directional fiber; SHPB; dynamic impact Article
## 4 Results
### Evaluation of ConceptNet with Domain-Specific Concept Filtering  
ConceptNet collected knowledge from many sources, including Wiktionary, WordNet, etc., [27]. The concepts in ConceptNet vocabulary can be very trivial and can be distant from the real subject-wise educational domain. Therefore, when transforming ConceptNet's concept embeddings to representative concept-based sentence embeddings, some \"concepts\" in ConceptNet's vocabulary may not be educational domain concepts, possibly resulting in the sentence embeddings becoming diluted in their representation.  
Based on this, we explore adding an extra layer to filter the identified concepts from the ConceptNet vocabulary. Such a concept filter can be built by training a domain-specific concept extraction model [59; 60], or using some pre-trained \"concept\" extractor such as TagMe (entity linker) [61], DBpedia Spotlight (DBpedia hyperlinks), or TopicRank (keyphrase extraction) [62]. When it comes to terminology, there are some subtle differences for the real meaning for mentions, keywords, named entities, knowledge graph entities, concepts, educational concepts, or domain-specific educational concepts, which are the outputs from these \"concept\" extractors. Therefore, the objectives from different \"concept\" extractors can be different. Here, we instead use the gold-labeled concepts in the digital textbooks to construct a subject-wise domain concept dictionary. Figure 6 illustrates the idea of the ConceptNet model with domain-specific concept filtering. We posit that these filtered concepts are more relevant in the context of the input texts compared to the complete mapping of all concepts from the ConceptNet vocabulary.  
Table 5 shows the results of using ConceptNet Numberbatch with domain-specific concept filtering. However, compared to using ConceptNet Numberbatch alone, domain-specific concept filtering does not improve the results. Figure 7 shows how textbook content is mapped to ConceptNet. It seems that almost all the words can be mapped, but \"naturvetenskapliga\" (English translation: natural scientific) as a compound word can only be found separately, although this is a common word in the Swedish language. Similarly, \"[PERSON]\" is tagged word by word without having the famous whole name as a concept phrase. Some words (\"stamband\", \"fortating\", and \"fortunning\") (English translation: \"vocal cords\", \"thickening\", and \"thinning\") are lacking inflectional forms. The results indicate that: (1) other non-core domain conceptual words can also provide useful information to the sentence embeddings, which explains why the original ConceptNet Numberbatch model performs better; (2) the training data of ConceptNet for the Swedish  
Figure 6: Illustration of using the ConceptNet Numberbatch model with domain-specific concept filtering. * CN-NB = ConceptNet Numberbatch.  
language is not large enough to cover all words' inflectional forms, compound words, and phrases.
## 4 Discussion
The scanner from GeoSLAM for scanning bridge structures or other concrete structures is discussed, e.g., in [PERSON] et al. [38] and [PERSON] et al. [39], but unfortunately, they only show the possibilities of use without testing the accuracy of the resulting cloud. Interesting accuracy testing can be found in [PERSON] et al. [40], where the beginning of the bridge structure is scanned using a Zeb Horizon scanner from GeoSLAM. The accuracy of the resulting data on the tested piece of structure is also consistent with the accuracy we report here for the original data without MLS or ICP. Unfortunately, the entire bridge was not scanned in the experiment and thus it is not possible to discuss the accuracy of the entire structure. The accuracy characteristics achieved in our study are, considering the character of the object of the study and manufacturer-provided scanner parameters, very good. It should be noted that a bridge may not be an optimal object for SLAM scanning as it only covers a small part of the scanner view field. The original SLAM scanner data were characterized by a deviation from the reference cloud of approximately 0.02 m, which is a result sufficient for many applications, such as the creation of digital twins. Denoising SLAM scanner-acquired data using the MLS algorithm appears to be a useful approach, facilitating the processing and interpretation of data. When considering local accuracy after local (individual profiles) ICP transformation, i.e., evaluating the shape accuracy, the scanner yielded _RMSDs_ as low as <0.01 m.  
In terms of cost-effectiveness, the acquisition costs of SLAM scanners are nowadays comparable to those of terrestrial scanners. A SLAM scanner yields somewhat poorer accuracy (which may, however, be sufficient for many applications). On the other hand, the speed of data acquisition is a significant bonus, as the entire bridge structure was surveyed in about 30 min using the SLAM scanner compared to about 3 h when using the static terrestrial scanner.