esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Information as a Variable in Media Ecology Analysis
### Time  
Transmission of information is generally discussed in regard to space, and the speed at which distance can be bridged, but transmission can also occur over time [20], a process otherwise known as information storage. In a sense, information needs to be stored for at least the length of time required for its transmission, even if it is just an instant, but it is not speed, but rather duration and durability that is the concern here. Languages store information in the code itself, by identifying what phenomena in the environment are worthy of being named and therefore need to be attended to, and by providing the grammar to properly interpret messages [43, 44, 45]. Symbolic communication provides a unique potential for what [PERSON] referred to as time-binding, the ability to communicate information over time, from one generation to the next [14]. The spoken word, being ephemeral, is a poor medium for storage, however, and collective memory is required to preserve language and its contents. Oral cultures clearly have limited capacity for information storage, and fidelity is likewise limited in transmission over time, although this has the virtue of providing flexibility [20] and aiding in homeostasis [22, 46].  
Notational systems, and especially the written word vastly increase our storage capacity, which is why they are intimately associated with the shift from tribal society to what has traditionally been termed civilization. Durability is an important factor in storage capacity, so that monuments and architecture can be highly effective forms of information storage, and writing on stone and clay tablets, and parchment manuscripts, all of which [PERSON] termed heavy media, help to transmit information accurately over time, albeit at the cost of reduced flexibility, increased rigidity of culture [20]. Copying of written texts provides new forms of flexibility and new sources of noise in transmitting information over time, in the form of scribal corruption. Printing, in producing multiple, identical copies of a text, aided in the storage of information and eliminated the multiformity of oral and scribal transmission [40]. By the 19 th century, information storage led to an unprecedented historical consciousness in the western world, further enhanced by new capacities to store visual, audile, and audiovisual information. Although broadcasting is exceedingly ephemeral, technologies for electronic recording have proliferated, and digital technologies coupled with computer databanks represent the possibility of _total recall_, a prospect at once exciting and worrying for its surveillance implications [47]. Digital archives promise unprecedented access to stored information, but concern exists that changing formats may make older archives inaccessible. Even data stored in digital form is dependent on certain types of software needed to read the files, a form of metainformation, and there already are many instances where new versions of programs cannot read the files produced by older versions of the same program.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Biochemical Approaches to Identify mTOR Signaling Pathway Components
#### 4.1.11 Rag GTPases Were Identified as mTOR1 Regulators Using a Modified Raptor-IP Protocol  
Due to the presence of heavy and light chain signals in canonical antibody-based immunoprecipitations when resolved on SDS-PAGE, [PERSON] et al. speculated that some mTOR1 associating proteins might be masked by signals from antibody-derived heavy and light chains [47]. To this end, Flag-Raptor immunoprecipitations were obtained from 30 million HEK293T cells stably expressing Flag-Raptor and eluted with Flag peptides. Resulting proteins were resolved by SDS-PAGE, silver stained, and the 45-55 KD region (where the heavy chains locate) was excised and digested with trypsin for liquid chromatography and mass spectrometry analyses. [PERSON] et al. found a 44 KD protein RagC as a Raptor/mTOR1 associating protein. Further biochemical studies revealed that a heterodimer composed of GTP-loaded RagA/B and GDP-loaded RagC/D activated mTOR1 in responding to amino acids [47].  
1.12 Ragulator (MP1, p14, and p18) Was Identified as a Rag GTPase Binding partner by Rag-IPs to Recruit Rags to Lysosome  
Considering that Rag GTPases localize to lysosome in an amino acid-independent manner [48], and Rag proteins lack the lipidation sequence that helps to recruit Rags to lysosome, [PERSON] et al. hypothesized that unknown protein(s) facilitates anchoring of Rag GTPases to lysosome. Mass spectrometry analyses of Flag-RagB or Flag-RagD immunoprecipitations from HEK293T cells led to identification of a trimeric protein complex composed of MP1, p14, and p18 that was termed as \"Ragulator\" as novel Rag GTPases binding partners. It was shown that the p18 N-terminal myristoylation and palmitoylation were critical for p18 localizing to lysosome membrane to recruit other Regulator components and Rag GTPases to lysosome [48].
# New Materials and Effects in Molecular Nanomagnets
## (1) Ferromagnetic-chain strategy
### (2) Ferrimagnetic-chain strategy
Both of these 2p-3d-4f heterotrispin systems made of chains from nitronyl-nitrooxide show a single-chain magnet behavior with a slow magnetic relaxation below 3 K [128]. Another shape of the chain was found by [PERSON] et al. in nitronyl-nitrooxide organic radical chains. In [Tb(hfac)\({}_{3}\)NIT-O-Hexyl]\({}_{n}\) latter structures, with hfac\({}^{-}\) = hexafluoroacetylacetonate, chiral curled chains were formed, building supramolecular tubes with a diameter of 4.5 nm and large coercive fields, as visible in Figure 7 [129]. [PERSON] et al. found slow relaxation and the typical temperature-dependent out-of-phase AC magnetic susceptibility pointing out SCM behavior in the chain complexes [Mn\({}_{2}\)(salen)\({}_{2}\)(L)](ClO\({}_{4}\)) and [[Mn(salen)\({}_{2}\)(L)\({}_{2}\)]-Y, with salen = \(N\),\(N^{\prime}\)-bis(salicylidene)-ethyl eediamine, L = 3,4,5-trifluorobenzeneeseleninic acid, and Y = salicaldehydehyde, which is unusual for aromatic selenite-based materials [130]. Another unusual SCM was produced by [PERSON] et al. who prepared homospin Co\({}^{\Pi}\) ferrimagnetic SCMs in the form of [Co\({}_{3}\)(L)\({}_{4}\)(OH)\({}_{2}\)(H\({}_{2}\)O)\({}_{2}\)]\({}_{n}\) with L = 4-(tetrazol-5-yl)phenyl-4,2'.6',4''-terpyridine). These 1D chains exhibited triangular non-uniform chains, bridged by ligands to form a 2D coordination framework. This system showed an SCM behavior with large hysteresis loops and activation energies varying for infinite- or finite-chain regimes [131]. Co\({}^{\Pi}\) ions were also included in the 1D coordination polymer [[Co\({}_{2}\)(pymca)\({}_{2}\)-(H\({}_{2}\)O)\({}_{4}\)]SO\({}_{4}\cdot\)2H\({}_{2}\)O]\({}_{n}\) with pymca = 2-carboxypyrrimidine, which showed an SCM behavior below 3 K [132], and in Co\({}^{\Pi}\) metal-organic frameworks with a strong magnetic 1D anisotropy, applying different bridging ligands [133]. Besides the aforementioned examples, many other variations of SCMs can be found in the recent literature, based on diverse physical principles, combining a broad variety of materials and shapes. Here, as well as in SMMs, many more highly interesting new findings can be expected in the next years.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 2 Background
### Dataset Description  
In this work, we use the dataset processed by [PERSON] et al. [19] as our baseline. This dataset contains data from 670 confrontation matches in AAGWS and consists of seven files that record comprehensive game information. Each game is documented in detail, including the initial state of each piece at the beginning (covering basic attributes such as position and type) and the state of each piece at the end of the game (indicating whether it is alive or in another state). Additionally, the dataset records the final status of occupied points, specifying which side controls each point. It also includes action records for each step taken by the pieces throughout the game, detailing movement paths, attack behaviors, and other operations. After parsing the dataset, the extracted features in AAGWS are categorized into attribute vectors and spatial tensors. The attribute vectors describe the attributes and action stages of the pieces, while the spatial tensors represent the situational data of the wargame. To distinguish between tensors and vectors, we define tensors in this context as arrays with dimensions greater than or equal to two. A detailed composition of these features is shown in Table 1.  
The attribute vectors are one-hot encoded and consist of a total of 301 bits. Most of the spatial tensors also use one-hot encoding, except for the elevation of the map, which is normalized to values between 0 and 1. The spatial tensor has a shape of \(25\times 66\times 51\) in total. Due to only incomplete information being available for each side, the data features for the two opposing sides differ. After extracting the wargame features, we divided the dataset into training and test sets in a ratio of 8 to 2, ensuring that the ratio of winners to losers remained balanced at \(1:1\) in both sets (the data includes perspectives from both the red and blue sides).  
Figure 1: The wargame map of AAGWS. The hexagonal grid represents the smallest unit of the map. Each grid contains two kinds of information: the location and grid properties.
## 3 Analysis of Deformation Characteristics of Underground Structure
### Existing Structure Deformation Analysis  
Figure 6 compares the results of numerical predictions, field monitoring, and equivalent calculations for the deformation of an existing structure due to a new tunnel undercrossing. The figure shows that the structural settlement curve of the hollow structure, calculated by equating the foundation beam to the thickness (1D), aligns well with measured and simulated results.  
The settlement curves for Projects 1 to 7, calculated based on overall structural equivalence, differ significantly from measured data due to excessive stiffness. In Project 1, the existing structural floor deformation abruptly changes at expansion joints and distributes linearly. Notably, a distinct difference arises between Projects 1 and 2 when the structure is equivalent to twice the thickness of the floor or twice the thickness of the wall (2D). Conversely, Projects 3 to 7 show good matching. The settlement curves for Projects 5 to 7, computed by equivalent foundation beams according to 1D and 2D, respectively, coincide. This is because minor stiffness increases in an elastic foundation beam with low stiffness do not significantly alter deformation until a critical stiffness value is reached.  
By comparing Project 2 and Project 4, it can be observed that, based on the 2D equivalent foundation beam versus the 1D, the deformation of the existing structure is reduced by 39.4%, 4.22%, and 3.26%, respectively. These results suggest limited deformation reduction for Project 3 and Project 4. Considering the existing structural stiffness data in Table 4, structural deformation calculated from small changes in foundation beam stiffness only occurs when the stiffness reaches 1.0 \(\times\) 10\({}^{9}\) N\(\cdot\)m\({}^{-2}\). Thus, determining the equivalent stiffness of foundation beams solely from existing structural deformation is insufficient. Integrating these results into force analysis is necessary.
## 4 Results and Discussion
### Effects of Instantaneous Overtopping  
Besides the slamming phenomenon, the overtopping is another tough problem for semi-submerged cylinder in an oscillatory flow [18; 19; 24]. In Section 4.3, an unexpected drop of the lift coefficient was observed in Figures 14 and 15. To further investigate the hydrodynamic mechanism behind this phenomenon, the water surface waveforms, the corresponding water surface sketches at the maximum forced motion velocity, the time histories of forced motion and hydrodynamic force in both CF and IL directions are compared and shown in Figure 17. Under the same Stokes number, the upstream water level of the cylinder keeps rising with KC number increasing as shown in Figure 17a,e,i,m. To clearly illustrate the water level change, the corresponding sketches are shown in Figure 17b,f,g,n. The symbol \"H\" represents the upstream water level. When KC = 37.70, the upstream water was over the top of the semi-submerged cylinder, which is termed as \"overtopping\" phenomenon [18; 19]. Different from that in steady flow, the overtopping only happens at around the time of forced motion velocity amplitude. We redefine the phenomenon as \"instantaneous overtopping.\" Combining with the lift coefficient distribution, we observe that the drop phenomenon happens as the instantaneous overtopping occurs. The reason can be attributed to the changes of the flow field asymmetry. When the overtopping occurs, this asymmetry weakens, which results in a reduction in the lift coefficient. The same phenomenon was also found in the results under the steady flow reported by [PERSON] et al. (2019) [19]. However, the effects of overtopping in an oscillatory flow were not seen in drag and added mass coefficients. Moreover, the drag force and lift force appear relatively high frequency fluctuation in the peak area as shown in red rectangle part of Figure 17c,d,g,h,k,l,o,p. The fluctuation of the hydrodynamic force is also attributed to the instantaneous \"overtopping\" phenomenon at forced motion velocity amplitude. The extreme surface distortions and water fragmentation caused by the instantaneous \"overtopping\" phenomenon result in this higher frequency hydrodynamic force. Limited by the quantity of experimental data, more systematic experiments will be conducted to study these effects of instantaneous \"overtopping\" phenomenon.
## 3 Methods
### Preliminaries of DARTS  
Early existing architecture search algorithms are computationally like NAS ([PERSON] et al., 2015) based on reinforcement learning that obtains a state-of-the-art architecture for CIFAR-10 required 2000 GPU days. In order to speed up the search process, DARTS ([PERSON] et al., 2015) has proposed relaxing the discrete search space to a continuous search space so that the architecture parameters can be optimized by gradient descent.  
Each node \(x^{(i_{f})}\) is a latent representation such as a feature map in convolutional networks and there are several edges \((i,j)\) between every two nodes during the search phase. Each edge is an operation \(o^{(i_{f})}\) of the search space \(O\) (e.g., \(3\times 3\) dilated convolution, \(3\times 3\) max pooling) and a directed edge represent a transformation from \(x^{(i)}\) to \(x^{(j)}\) by applying operation \(o^{(i_{f})}\). Thus, each intermediate node is computed as:  
\[x^{(j)}=\sum_{i<j}o^{(i_{f})}(x^{(i)}) \tag{1}\]  
The target of DARTS is to find the optimal operation (including a special zero operation that indicates a lack of connection between two nodes) between every two intermediate nodes, and then derive the final architecture that performs well in target datasets. Specifically, as showed in Figure 2, DARTS first initialize a supernet with random network weights and unknown operations on the edges. Then, there is continuous relaxation of the search space by placing a mixture of candidate operations on each edge to get a continuous search space that can optimize validation loss by using gradient descent. The process of making the search space continuous can be described as:  
\[\hat{x}^{(i,j)}=\sum_{o\in O}\frac{exp(\alpha_{o}^{(i_{f})})}{\sum_{o^{\prime }\in O}exp(\alpha_{o^{\prime}}^{(i_{f})})}o(x) \tag{2}\]  
where the \(\alpha\) indicates the architecture parameters.  
Let \(L_{t}\) and \(L_{\tau}\) denote the training loss and validation loss, after relaxing the categorical choice of a particular operation to a softmax over all possible operations, DARTS aims at jointly learning architecture \(\alpha\) and the network weights \(w\) by finding \(\alpha^{*}\) that minimizes the validation loss. The goal of DARTS is to reduce such a bi-level optimization problem:  
\[\underset{\alpha}{min}\ L_{v}(w^{*}(\alpha),\alpha) \tag{3}\]  
\[w^{*}(\alpha)=argmin_{w}\ L_{t}(w,\alpha^{*}) \tag{4}\]  
where the \(w^{*}\) is obtained by minimizing the training loss.  
Lastly, the learned \(\alpha\) will be used to derive the final architecture.
# A Systematic Review of Insole Sensor Technology: Recent Studies and Future Directions
## 4 Insole Sensor Technology
### Inertial Measurement Unit(s)  
Inertial measurement units are the most efficient techniques for measuring gait phases and events. These devices possess the qualities of portability, energy efficiency, cost-effectiveness, durability, and reliability [15]. IMUs combine accelerometers and gyroscopes to detect linear acceleration and angular velocity [33]. In addition to accelerometers and gyroscopes, certain IMUs also include magnetometers. These devices are inertial-magnetic or magneto-inertial measurement (MIMU) [1]. Driven by recent advancements in embedded technology within human-machine systems, IMU sensors are now embedded in smart and wearable devices such as shoes, watches, phones, and adhesive bandages to support daily human activities [17].  
Accurate and dependable real-time identification of gait events using IMUs are crucial for the development of clinically significant gait characteristics that can differentiate between normal and impaired gait. Precision is also essential for developing individualized gait rehabilitation techniques for patients and controlling prosthetic devices based on feedback from gait phases [17]. Achieving precise human motion localization using IMU sensors is difficult due to various issues, including sensor drift, the sensor's orientation with respect to the Earth, and magnetic interference from the surrounding environment [17].
## 3 Results and Discussion
### Adsorption and Desorption Experiments
#### 3.2.6 Adsorption Selectivity Study  
To evaluate the selective property of Cd-I-MA-_co_-AN and NI-MA-_co_-AN, various coexisting ions, such as Cu(II), Mn(II), Ni(II) and Pb(II) with a similar charge and similar ionic radius to Cd(II), were used as competing ions. The distribution coefficient (D), selectivity coefficient (\(\alpha\)) and relative selectivity coefficient (k) are presented in Table 3. The distribution coefficient reveals that Cd-I-MA-_co_-AN has a better adsorption performance for Cd(II) than that of Cu(II), Mn(II), Ni(II), and Pb(II). This is due to the size and shape of the holes left by the ion imprinted polymer after eluting, which matched Cd(II) and the imprinted polymeric microsphere containing active sites that allow for them to interact with cadmium ions. Cd-I-MA-co-AN has a stronger affinity for Cd(II) with an ionic radius of 95 pm than for Cu(II) (73 pm), Mn(II) (83 pm), Ni(II) (69 pm), and Pb(II) (119 pm) [39]. Thus, Cd-I-MA-_co_-AN has high selectivity for Cd(II).
# Two-Wire Power Line Microwave Communication Using Low-Loss Modes
## 4 Results
### Common vs. Differential Mode Solutions  
In Figure 4, results are shown for the magnitude of the current distributions at 1 GHz for the common and differential modes. Two methods are used for calculating the common mode current: the perfect conducting method of (47) and the lossy conductor case in (45). There is little difference between these two methods over typical communication ranges, as was the case in [22]. Hence, perfect conductor theory can be used. Second, in the lossy conductor case, both the continuous spectrum and the mSG modal component are important for typical communication distances. There is no important condition (for communications) for which the mSG mode dominates the continuous spectrum (at least for the source studied here). Note, however, that for the differential mode, the quasi-TEM mode dominates even at very short distances away from the source.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency (GHz)** & **Type of Pole** & \((\gamma_{P}-\mathbf{k_{0}})/\mathbf{k_{0}}\times\mathbf{10^{-6}}\) \\ \hline
1 & SG & \(9.1-\mathrm{j}10.3\) \\ \hline
1 & mSG & \(5.9-\mathrm{j}7.0\) \\ \hline
1 & Quasi-TEM & \(17.2-\mathrm{j}17.2\) \\ \hline
10 & SG & \(3.8-\mathrm{j}4.5\) \\ \hline
10 & mSG & \(2.8-\mathrm{j}3.7\) \\ \hline
10 & Quasi-TEM & \(5.5-\mathrm{j}5.6\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Modal propagation constants for a \(=1\) cm, \(\sigma_{\mathrm{w}}=3.5\times 10^{7}\) S/m, d (for mSG and Quasi-TEM) \(=0.5\) m.  
Figure 5 illustrates that (52) is a reasonable approximation for the total common mode current, (44), with \(a\rightarrow\sqrt{ad}\), especially at lower microwave frequencies. When this substitution is made, the branch cut integration of (40)-(43) can be avoided.  
Figures 6 and 7 are plots of the individual wire currents excited by a voltage generator at z = 0 on wires 1 and 2, as well as the single wire current from [22] for frequencies of 1 and 10 GHz, respectively. As expected, the current on wire 1 exceeds that on wire 2 in the two-wire case. In addition, of interest is the fact that the current on wire 1 at 100 m for the two-wire case is significantly higher than that on the single wire. This indicates that the performance of a microwave power line communication system can be enhanced by the existence of parallel wires. This conclusion is consistent with that reported in [7]. Additionally, when approximations of (52)-(54) can be used, very simple expressions for the current result. This is more commonly true for \(\hat{I}_{1}(z)\), which has smaller errors and is also more important for evaluating communication systems.  
Figure 4: Plot of common and differential mode currents for Table 1 parameters and f = 1 GHz.  
Figure 5: Comparison of the full spectral solution to the approximate solution of (26) when the geometric mean radius of the two wires \(\sqrt{ad}\) is substituted for the individual wire radius a. The parameters are given in Table 1.
## 1 Introduction  
While the recognition and quantification of fluvial transport and depositional processes have been studied for many years, few works have focused on the description, characterization and interpretation, and quantification of sedimentary processes in low-energy fluvial environments. The study of these environments presents challenges for sedimentological work due to their high degree of homogeneity at the macroscopic scale, low particle size contrast (silt-clay deposits), and the absence of microscale stratigraphic features (laminations). To provide a first insight, this paper discusses the results of five approaches to processing grain size data and evaluates their effectiveness in characterizing low-energy alluvial plain deposits. The question is: to what extent can conventional or more recent grain size processing methods be used to decipher this type of sedimentary record?  
This study focuses on and compares the results obtained using five different methods: statistical moments, textural analysis, CM image analysis, multivariate statistics employing successive Principal Component Analysis and Hierarchical Cluster Analysis (Appendix A)on various data sets, and an end-member approach (EMMAgeo). These methods were applied to an approximately 9 m-long core extracted from the fluvial island of la Baine, located downstream of the Charente River near Saintes in Charente-Maritime, France.