esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Properties of Hardened Mortar  
#### 3.2.1 Flexural Strength  
Figure 10 shows the measurement results of flexural strength at the age of 28 days of conductive CFRCM and SFRCM according to the variation in fiber volume fractions compared to PM. As indicated in Figure 10, the average flexural strength of PM was measured to be 3.76 MPa, and as the volume fraction of carbon fibers increased, the flexural strength showed a tendency to decrease somewhat. In the case of CFRCM containing 1.25% fiber, particularly, the flexural strength decreased rapidly. On the other hand, SFRCM showed significantly higher flexural strength as the fiber volume fractions increased. In the case of CFRCM containing 0.5% fiber obtained from this test result, the flexural strength increased approximately by 1.62 MPa compared to PM, and CFRCM containing 0.75% fiber turned out to increase by about 1.45 MPa. Furthermore, in the case of CFRCM containing 1.0% fiber, the flexural strength increased by about 1.06 MPa compared to PM, and CFRCM containing 1.25% fiber was almost similar to 0.01 MPa. Accordingly, in the case of CFRCM containing 0.5%, 0.75%, 1.0%, and 1.25% fibers, the flexural strength was improved by about 43.1%, 38.6%, 28.2%, and 0.3%, respectively, compared to PM. The flexural strength was somewhat improved until the volume fraction of carbon fibers reached 1.0%, but the incremental effect was not so great when the volume fraction of carbon fiber was 1.25%. It is apparent that this has low flexural strength because it is not good in terms of dispersibility and workability due to the incorporation of a large amount of carbon fibers. These findings have also been reported in previous studies [9, 56]. In the case of SFRCM containing 0.5% fiber, however, the flexural strength increased by about 1.51 MPa compared to PM, and SFRCM containing 0.75% fiber increased almost by 2.26 MPa, Moreover, in the case of SFRCM containing 1.0% fiber, the flexural strength increased almost by 3.61 MPa compared to PM, whereas SFRCM containing 1.25% fiber increased by about 3.41 MPa. Therefore, in the case of SFRCM containing 0.5, 0.75, 1.0, and 1.25% fibers, the flexural strength was improved by about 40.1, 60.1, 96.0, and 90.7%, respectively, compared to PM. This is thought to be because the steel fiber played a bridging role to prevent crack propagation and to improve flexural strength through stress redistribution.
## 2 Literature Review
### Long Short-Term Memory (LSTM) Model  
Recurrent neural networks (RNN) are a suitable model for time series forecasting problems. However, RNN are not suitable for long-term dependency tasks due to the vanishing/exploding gradient decent issues [39]. Therefore, the LSTM neural network arises, which can learn the long-term dependency jobs very efficiently compared to the general RNN model [40]. The LSTM model solves the vanishing/exploding gradient decent issues with gates present within each cell of an LSTM network [41; 42; 43]. LSTM is one of the popular artificial recurrent neural network architectures used in wind speed prediction [44]. The LSTM neural network was first proposed by [PERSON] and [PERSON] [45]. An LSTM cell's internal state memory offers the internal storing of pertinent historical information. The flow of information through the cell is controlled by the cell's input, output, and forget gates and the mathematical implementation of each LSTM cell is described using Equations(17) to (22). With the help of these gates, LSTM analyzes and saves pertinent data [46]. The phrase stacked/deep LSTM is often used to denote the LSTM network, referring to an LSTM network with two or more hidden layers. An LSTM network with a detail structure of an LSTM cell is shown in Figure 1.  
I Step: Forget layer  
\[f_{t}=\varnothing(w_{f}.[y_{t-1},x_{t}]+\beta_{f} \tag{17}\]  
II Step: Update of new values (\(I_{t}\)) and creation of a vector of new information (\(\widetilde{g}\)) to add to the cell state.  
\[I_{t}=\varnothing(w_{i}.[y_{t-1},x_{t}]+\beta_{i}) \tag{18}\]  
\[\widetilde{g}=\tanh(w_{i}.[y_{t-1},x_{t}]+\beta_{s}) \tag{19}\]  
III Step: Final cell state  
\[g_{t}=f_{t}.g_{t-1}+I_{t}\cdot\widetilde{g} \tag{20}\]  
IV Step: Last stage using a sigmoid function and a tanh, regenerating values between \(-1\) and \(1\).  
\[\partial_{t}=\varnothing(w_{\partial}.[y_{t-1},x_{t}]+\beta_{\partial}) \tag{21}\]  
\[y_{t}=\partial_{t}.\tan\mathrm{h}(g_{t}) \tag{22}\]  
In Equations (17) to (22), \(x_{t}\) is the input, \(\widetilde{g}\) is the state of the network, \(g_{t}\) is the temporary state and \(y_{t}\) is the output state at time step t. \(I_{t}\) denotes the input gate, \(\partial_{t}\) represents the output gate and \(f_{t}\) denotes the forget gate. The weight corresponding to the hidden layer, input layer, and output layer is denoted by \(w_{f}\), \(w_{i}\), and \(w_{\partial}\), respectively. \(\beta_{f}\), \(\beta_{i}\)\(\beta_{s}\), and \(\beta_{\partial}\) represent bias corresponding to the input, sate of network, temporary state, and output layer of the network. \(\varnothing\) and \(\tanh\) represent the sigmoid and \(\tanh\) activation function, which is defined by the following expressions:  
\[\varnothing\ (z)=\frac{1}{1+e^{-z}} \tag{23}\]  
\[\tan\mathrm{h}(z)=\frac{e^{z}-e^{-z}}{e^{z}+e^{-z}} \tag{24}\]  
Figure 1: LSTM network and detail structure of an LSTM cell.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 2 Related Work
### IoT Services Composition and Coordination
They developed a heuristic-based composition approach with multiple local knapsacks to select an optimal set of energy-based services that devices in the IoT can deliver. [PERSON] and [PERSON] [38] analyzed the fundamentals of service composition mechanisms in the IoT to determine scalability requirements. Likewise, they identified that data flows, orchestration, and choreography do not fully satisfy such requirements, unlike the novel composition mechanism DX-MAN. Likewise, [PERSON] et al. [39] presented an incentive-based framework for energy service request composition. In addition, they designed an incentive model that considersthe context of providers and consumers to determine the rewards generated by wireless energy sharing. Concerning service coordination, in their work, [PERSON] et al. [40] presented a case study for the design of secure IoT applications to relate the semantics of event-condition-action (ECA) rules based on automata used for verification of rule compilation at run-time. Furthermore, [PERSON] et al. [41] presented IoT applications in smart cities from a distributed coordination model supervised by components. [PERSON] et al. [42] proposed a service platform for the IoT based on SOA to design services oriented to events. Besides, an event definition language (SEDL) was presented, based on automata, event detection algorithms, and a situational event driven by the service coordination behavior model. [PERSON] and [PERSON] [43] presented a study describing the behavior of the layers of flexible and multiple architectures for efficient IoT services coordination. Instead, they used the orchestration or choreography of services to achieve their coordination. However, this design decision posed considerable complexity since the coordination of services represented several challenges such as heterogeneity, accessibility, context awareness, and discovery. Moreover, [PERSON] et al. [44] proposed an agent-based approach to support large-scale IoT by providing complex integrated services. They avoided the common bottlenecks of this approach and the excessive bandwidth use due to direct communications in peer-to-peer (P2P) networks. Likewise, [PERSON] and [PERSON] [45] described a recent review of existing service coordination approaches in the IoT where they identified a tendency to use orchestration or choreography to meet this challenge. They presented a classification and overview of the leading service coordination models in the IoT. They designed an architecture in the context of the IoT that can achieve service coordination through the concept of meta-workflows, combining both coordination approaches: orchestration and choreography.
# Antisense-Mediated Down-Regulation of Factor V-Short Splicing in a Liver Cell Line Model
## 2 Materials and Methods
### Antisense Oligonucleotides  
Morpholino antisense oligonucleotides (MAOs) are nucleic acid analogues in which DNA bases are bound to a non-charged backbone (morpholine rings linked by phosphorodiamide bonds) [31]. MAOs targeting the FV-short-specific donor splice site (5\({}^{\prime}\)-CAAGGTTATTGACAGTAACTACT-3\({}^{\prime}\), Donor-MAO) and acceptor splice site (5\({}^{\prime}\)-AGGTCTGATAAGGAAAGACTCAT-3\({}^{\prime}\), Acceptor-MAO) were designed. Specificity for the intended target sites was verified by BLAST analysis against the human transcriptome. Another MAO with an irrelevant sequence (5\({}^{\prime}\)-CCTTACTACTCAGTTACAATTATA-3\({}^{\prime}\)) was used as a negative control. All MAOs were purchased from GeneTools (Philomath, OR, USA).
# _Forentation_
## 4 Discussion
### Viable Cell Counts and Antagonistic Activity  
Cell population dynamics are critical factors influencing fermentative efficiency, competition between strains, and the contribution of each yeast's metabolism to the final beverage. Therefore, the antagonistic interactions between yeast strains and the viable cell count of each strain throughout the fermentation were evaluated in the fermentation trials.  
The antagonistic activity of yeast strains with potential for use as starter cultures is considered a relevant technological attribute [45]. Strains with antagonistic capabilities can inhibit the growth of wild _S. cerevisiae_ strains during natural fermentations, thereby enhancing their implantation capacity. Since non-Saccharomyces yeasts, such as _P. kudriavzevii_ in this study, are usually employed as mixed starter cultures in combination with _S. cerevisiae_, it is essential to evaluate the potential inhibition of the non-Saccharomyces strain by _S. cerevisiae_ during co-fermentation. Interestingly, the _S. cerevisiae_ strain used in this study exhibited antagonistic activity against the sensitive reference strain (C. glabrata NPCC106), confirming its killer phenotype. However, it did not display killer activity against _P. kudriavzevii_ NPCC1651. Furthermore, the _P. kudriavzevii_ NPCC1651 strain showed no antagonistic activity but demonstrated resistance to both _S. cerevisiae_ NIF8 and the K2-type killer reference strain. This lack of sensitivity aligns with previous studies reporting the resistance of many Patagonian _P. kudriavzevii_ isolates to the antagonistic activity of _S. cerevisiae_ killer strains [46]. Certain _P. kudriavzevii_ strains have been reported to inhibit the growth of bacteria and fungal pathogens that cause post-harvest diseases in fruits, such as apples and citrus [47; 48; 49]. This antifungal capacity highlights the potential to further explore the antagonistic properties of the _P. kudriavzevii_ NPCC1651 strain.  
Additionally, _P. kudriavzevii_ is well-documented for its multi-stress tolerance, including resistance to acidic conditions, ethanol, salt, sulfites, and high temperatures [16; 31], all of which are desirable attributes for yeast strains in the cider industry.  
In line with these results and previous reports, viable counts of both strains were successfully detected at the end of all the mixed fermentations. This finding aligns with the limited studies, exclusively conducted in wine, that analyzed microbiological counts of co-cultures of _P. kudriavzevii_ and _S. cerevisiae_ strains [17; 23]. The combination of the killer activity of _S. cerevisiae_ NIF8 and the resistance of _P. kudriavzevii_ to this antagonistic activity is a noteworthy feature, making them a reliable pairing for mixed cultures in cider production.
## 3 Proposed Solution to Predict the AoA
### Beamforming and Precoding Strategy  
Accurate beamforming and precoding are essential to fully exploit spatial degrees of freedom in multi-antenna systems, especially under high-user-mobility and urban multipath conditions. In the proposed system, the integration of real-time AoA prediction (via LR), SVR-based refinement at the DU, and localised MUSIC estimation enables a dynamic and adaptive beamforming strategy that is tightly coupled with the O-RAN Split 7-2x architecture.  
This section outlines the beamforming and precoding methodology employed, the mathematical formulation of weight generation using predicted AoAs, and the considerations for interference mitigation in multi-user scenarios.
# Satellite Image Categorization Using Scalable Deep Learning
## 2 Related Work
[47] presented a semi-supervised land cover classification of remote sensing imagery using CycleGAN and EfficientNet. The related work is summarized in Table 1. Satellite image classification for land cover using traditional methods involves manual feature engineering and conventional machine learning classifiers. However, these methods may struggle with complex and high-dimensional data. To address this, deep learning approaches, such as CNNs, have been utilized and have shown robustness in various computer vision problems, including satellite image classification. Inception-Resnet, a state-of-the-art model, has demonstrated a high accuracy of over 99% for ship detection in satellite images. Other studies have also employed deep learning techniques for analyzing weak labeling, change detection, and land cover classification using satellite images. The use of EfficientNet further enhances the potential of the proposed methodology for accurate and efficient satellite image classification. The presented methodology achieves encouraging results on a challenging dataset with a high inter-class similarity and intra-class variation. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Problem** & **Feature Extraction Approach** & **Machine Learning Model** \\ \hline [PERSON] et al. [28] & Forest attribute categorization & Handcrafted & KNN \\ \hline [PERSON] et al. [29] & Land region classification & Handcrafted & SVM \\ \hline [PERSON] et al. [30] & Forest type classification & Handcrafted & KNN, MLP \\ \hline [PERSON] et al. [32] & Terrain feature extraction & Swarm optimization & - \\ \hline [PERSON] et al. [34] & Land cover classification and segmentation & Deep learning based & Unet \\ \hline [PERSON] et al. [36] & Change detection & Deep learning based & GRU auto-encoders \\ \hline [PERSON] et al. [37] & Building footprint extraction & & CGAN \\ \hline [PERSON] et al. [38] & Scene classification & Deep learning based & CNN \\ \hline [PERSON] et al. [39] & Land area classification & Deep learning based & CNN \\ \hline [PERSON] et al. [44] & Ship detection & Deep learning based & Inception-Resnet \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of Existing Methodologies.
## 4 Experimental Results  
### Experiment Setup  
To estimate the performance of our proposed method, we use IBM's Quest synthetic data generator [27] to generate the workload. The data were based on general transaction record databases, including the number of transactions (D), average frequent itemset length (I), number of items (N), and average transaction length (T).  
This experimental environment comprised 13 computers to execute an experimental analysis, one kernel node, one connection node, one monitor node, and other computing nodes for distributed computing. Each node was equipped with an Intel(R) Core(TM) i7-2600K CPU @3.40 GHz, 8 GB of memory (Intel Corporation, Santa Clara, CA, USA), 500 GB of storage executing on Windows 7 Enterprise Edition operating system (Microsoft Corporation, Redmond, WA, USA). The programs were written in Java, and the communication between nodes used Java Remote Method Invocation and socket.  
To evaluate the performance of FDCNB-MINING, we compared it with FLR-mining [10] by conducting a series of experiments. FLR-mining is a known efficient method for mining frequent patterns in such environments. We used the same dataset settings as [10] for a fair evaluation, so our basic dataset for experiments is D100K I5 N100K T20 under varying support. In addition, we used two real data sets for experiments under varying levels of support. The data sets are respectively retail-market [28] and accidents [29] data, which were downloaded from frequent itemset mining implementations (FIMI) [30].  
We simulated variation in network bandwidth as shown in Table 1. We simulated six different network bandwidth cases in computing nodes. In real applications, FDCNB-MINING can use the performance index of bandwidth \(B_{p}\) to measure the bandwidth value at each time point; for simplicity in the performance discussion, the bandwidth value from the kernel node to each computing node was as shown in Table 1, and the value was kept the same all the time in each case. Case 1 assumed that all computing nodes are in the 100 Mbps network bandwidth. Case 2-6 were randomly generated computing nodes in the 100-1 Mbps network bandwidth, and a bandwidth utilization rate of 80% was assumed.
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## 6 Instrument Noises and Corrections
### Timetag Errors
However, we note that the timetag error depends on the differential clock jitter between both satellites and our assumption of uncorrelated noise between satellites (ASD\([\delta t_{\text{c}}]\cdot\sqrt{2}\)) neglects potential common-mode rejection of errors that might appear between close-by GPS receiver. Moreover, for the sake of simplicity, we considered just a stochastic USO noise model and omitted potential sinusoidal variations due to the proper time and secular drifts due to uncertainties in the USO frequency for the calculation of the trace-labeled _timetag raw_. The proper time effect is not severe because the differential proper time (\(\tau_{\text{A}}-\tau_{\text{B}}\)) between satellites is much smaller than the individual contribution (\(\tau_{\text{A}}\) or \(\tau_{\text{B}}\)). We regard these approximations as acceptable, as the _timetag raw_ trace is anyway rather hypothetical due to the general availability of GPS data to correct the raw values.
## 4 Formation Controller Design
### Available Guidance Vector
\begin{cases}\frac{1}{|\mathcal{N}_{i}|}\sum\limits_{j\in \mathcal{N}_{i}}\left(\left(X_{i}-\bar{X}_{i}^{d}\right)-\left(X_{j}-\bar{X}_{ i}^{d}\right)\right)\text{, if }i\in\mathcal{V}-\mathcal{V}_{L}\\ \frac{1}{|\mathcal{N}_{i}|+1}\left(\sum\limits_{j\in\mathcal{N}_{i}}\left( \left(X_{i}-\bar{X}_{i}^{d}\right)-\left(X_{j}-\bar{X}_{i}^{d}\right)\right)+ X_{i}-\bar{X}_{i}^{d}\right)\text{, if }i\in\mathcal{V}_{L}\end{cases}  \tag{31}\]  
Then, according to definition of normalized interaction matrix in (2), we can rewrite Equation (31) in matrix form for all the quadrotors as follows  
\[ \begin{bmatrix}X_{1}-X_{1}^{d}\\ \vdots\\ X_{n}-X_{n}^{d}\end{bmatrix} =\bar{G} \begin{bmatrix}X_{1}-\bar{X}_{1}^{d}\\ \vdots\\ X_{n}-\bar{X}_{n}^{d}\end{bmatrix}  \tag{32}\]  
where \(\bar{G}\) represents the normalized interaction matrix. According to Corollary 2 and the definition in Equation (2), we know that \(\bar{G}\) is invertible if each connected sub-graph of the multi-quadrotor system has at least one leader. Therefore, the formation task is achieved, i.e., \(\left[X_{1}-\bar{X}_{1}^{d},\ldots,X_{n}-\bar{X}_{n}^{d}\right]^{T}\to\mathbf{0}\), as long as each quadrotor can precisely track the guidance vector \(X_{i}^{d}\) and \(Y_{i}^{d}\)(\(t\)), i.e., vector \(\left[X_{1}-X_{1}^{d},\ldots,X_{n}-X_{n}^{d}\right]\to\mathbf{0}\), and \(\left[Y_{1}-Y_{1}^{d},\ldots,Y_{n}-Y_{n}^{d}\right]\to\mathbf{0}\).
## 6 Results and Discussion
### Packet Retransmission  
Figure 11 shows the number of retransmitted packets for MP-EDCA and FASBA considered in the simulation. As shown in Figure 11, the proposed FASBA achieves lower retransmission attempts. If we look closely at Figure 11, we observe that the number of retransmitted packets increases with the increase in nodes for both schemes. The proposed FASBA achieved 60% lower retransmitted packets than MP-EDCA. This is achieved by optimising the block acknowledgement mechanism. The proposed FASBA can be used in wireless networks to achieve efficient retransmission. We didn't vary the mobility and channel configuration throughout. Therefore, the changes in retransmission are mainly due to channel contention of several emergency nodes.  
Figure 10: Mean MAC delay performance of MP-EDCA and the proposed FASBA.  
Figure 9: Mean MAC delay performance of the proposed FASBA.
# Image-Based Tactile Deformation Simulation and Pose Estimation for Robot Skill Learning
## 2 Materials and Methods
### Real-World TacTip Observation  
To develop a reliable model simulating Tactip's deformation, it is crucial to observe and record its behavior under various contact conditions. Overall, observations reveal that Tactip, unlike the GelSight sensor (GelSight, Inc., Waltham, MA, USA), exhibits broader deformation, spreading across the entire hemispherical tip when contacted with objects, while that of Gelsight is mostly concentrated at the small area in contact with the object. Also, as the Tactip has a hemispherical shape, the surface cannot closely fit to the surface of objects like GelSight.  
To investigate TacTip's real-world deformation, a Dobot Magician manipulator (Dobot Robotics, Shenzhen, China) equipped with two TacTips, one with standard black skin and the other with transparent skin, was used to interact with various 3D-printed objects, as shown in Figure 1. These objects, as shown in Figure 2, printed in identical pairs, were aligned with the centers of the TacTips on a base to ensure consistent deformation conditions. The manipulator's movement enabled the TacTips to contact the objects at varying positions and depths, with detailed deformation recorded. Video analysis revealed that the TacTip's contact area with a triangular prism-shaped object was linear due to limited skin contact at the object's peak, while with a semi-cylindrical, pillow-shaped object, the contact area was rectangular but smaller than the object's cross-section, suggesting incomplete surface conformity, as shown in Figure 3. These observations led to the formulation of three hypotheses regarding the TacTip's deformation behavior.  
Figure 1: Dobot Magician manipulator and customized dual-TacTip end effector setup.  
Figure 2: Three- dimensional-printed triangular prism part.  
**Hypothesis 1.**: _The strain on the Tactip skin is negligible: Tactip's hemispherical silicone rubber skin, filled with silicon gel, exhibits minimal localized strain compared to flat-layered sensors such as Gelsight. The strain is evenly spread around the entire tip, rendering it negligible._  
**Hypothesis 2.**: _The angle deformation on Tactip's surface is limited: Tactip's skin cannot fully conform to an object's surface beyond a certain angle change, leading to an imperfect fit. A specific maximum angle change rate on the object's surface can be identified, beyond which the Tactip skin would detach._  
**Hypothesis 3.**: _The deformation of the Tactip will fall into a stable and passive state in a static interaction: In such interactions, after contact, the TacTip tends to revert to its original shape, making the final shape predictable due to minimal deformation compared to its original form._