esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Use of Deep Learning for Video Classification
## 3 Video Classification
### Few-Shot Video Classification  
FEW-SHOT learning (FSL) has received a great deal of interest in recent years. FSL tries to identify new classes with one or a few labeled samples [80; 81; 82; 83]. However, due to most recent work in few-shot learning being centered on image classification, FSL in the video domain is still hardly being explored [84; 85]. Some of the notable works done in this domain are discussed below.  
A multi-saliency embedding technique was developed by [PERSON] et al. [85] to encode a variable-length video stream into a fixed-size matrix. Graph neural networks (GNN) were developed by [PERSON] et al. [86] to enhance the video classification model's capacity for discrimination. The local-global link in a distributed representation space was still disregarded nevertheless. To categorize a previously unseen video, [PERSON] et al. [87] introduced a temporal alignment module (TAM) that explicitly took advantage of the temporal ordering information in video data through temporal alignment. To combine the two-stream aspects of videos more effectively, [PERSON] et al. [88] developed a depth-guided adaptive instance-normalization module (DGAdaIN). A C3D encoder was created by [PERSON] et al. [89] to record close-range action patterns for spatiotemporal video blocks. Few-shot video categorization was addressed by [PERSON] et al. [90] by learning a collection of SlowFast networks enhanced with memory units. To comprehend realistic films of the target classes, [PERSON] et al. [91] presented embodied agent-based one-shot learning, which made use of synthetic videos created in a virtual environment. For the issues of few-shot and zeroshot action recognition, [PERSON] et al. [92] presented the temporal attentive relation network (TARN), which was trained to compare representations of varying temporal length. By examining local-global linkages and preserving the specifics of properties, [PERSON] et al. [93] recently presented a dual-routing capsule graph neural network (DR-CapsGNN) to address the issue of severely constrained samples in few-shot learning.  
Apart from this, contrastive learning has also proved successful in recognizing human actions. Some of the interesting works done in this regard are multi-granularity anchor-contrastive representation learning [94] and X-invariant contrastive augmentation and representation learning [95].
# Discrete HMM for Visualizing Domiciliary Human Activity Perception and Comprehension
## 2 Human Activity Comprehension Framework
### Pose Sequences for Human Activity  
To estimate a specific human activity from the sequential frames in a video clip, the feature set in each frame is treated as an observation symbol, such that an identical activity can be regarded as the composition of the sequential symbols, herein named pose sequences; for example, the summoning pose activity is interpreted by five sequential poses, as shown in Figure 4. However, the raw video contains numerous frames in which some noisy clips might influence the corrective pose predictions, and thus, lead to a worse performance. To overcome such a case, the features are clustered into sets in terms of pose classification through vector quantization in order to downsize the number of poses, and accordingly, given the feature set of each frame (\(F_{1}\), \(F_{2}\), \(F_{3}\),... \(F_{N}\)), vector quantization is applied to separate \(N\) observations into \(k\) sets.  
Figure 4: Example of the skeletonized “summon-pose” activity sequences divided into five sequential poses by time index accordingly.  
Figure 3: (**a**) Example of the relative position of the skeletonized summon pose in the Cartesian coordinate system; (**b**) transformation from the Cartesian into the Spherical coordinate system with corresponding azimuthal angle \(\theta_{k}\) and polar angle \(\varphi_{k}\).  
Several concerns regarding vector quantization (VQ) [29; 30; 31; 32] in this work are addressed below, given a vector source with its known statistical properties, such as \(F\) in Equation (11), given a distortion measure, and given the number of codevectors to find a codebook with minimized average distortion. In VQ, the set of quantized values for the vectors is a codebook. By quantizing each component of the source vectors, i.e., the feature set F of the frame, they can be subsequently substituted from a carefully chosen set and saved as an index set to yield a much more compact representation of the frame. The detailed steps of VQ are elucidated as follows:  
**Step 1**: Generate the training vectors from the video dataset. The training vectors are defined as in Equation (12):  
\[T\ =\ \{F_{n}|n=1,\ 2,\.\.\.\.\.\.\.\,\ N\} \tag{12}\]  
where \(N\) is the number of training vectors, i.e., the number of frames. Let \(M\) be the number of codevectors and let \(C\) represent the codebook in Equation (13) as follows:  
\[C=\{C_{m}|m\ =\ 0\,1,\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\.\
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 4 Design of Pido
### The Prediction of \(S^{{}^{\prime}}(t+\tau)\)
#### 4.3.2 Classifier  
For the sake of efficiency, we utilize a classifier for the prediction of \(S_{i}^{{}^{\prime}}(t+\tau)\), and compare three common classification algorithms: Logistic Regression [31], Ada Boost Classifier [32] and Gaussian Naive Bayesian [33]. Logistic Regression is an implementation-friendly algorithm, which is extremely lightweight in both time and space. Ada Boost Classifier utilizes several weak classifiers to vote for the result, and generally performs well in many classification cases. On the other hand, Ada Boost Classifier is too sensitive to the outliers, which means it may easily lead to over-fitting. Gaussian Naive Bayesian is one of the simplest classification algorithm and can perform well when the data fits Gaussian distribution. The detail of these three algorithms can be referred in [31; 32; 33]. The comparison of these three algorithms will be stated in Section 4.4. The model is defined as:  
\[\begin{split} Model\_1&=LogisticRegression(\;L,\;D_{av}, \;N_{nb},\;N_{link}),\\ Model\_2&=AdaBoostClassifier(\;L,\;D_{av},\;N_{nb},\;N _{link}),\\ Model\_3&=GaussianNB(\;L,\;D_{av},\;N_{nb},\;N_{link }).\end{split} \tag{6}\]  
Note that what we predict is the future status (\(\tau>0\)), and we should utilize the future electrode readings as the input. For example, a set of input sample is: \(\{L(t+\tau),\;D_{aw}(t),\;N_{nb}(t),\;N_{link}(t)\}\).  
Figure 6: The accuracy of node status prediction with the tidal periodicity.  
The mechanism of predicting future status is as follows:  
* **Data collection:** We collect training sets from several tide periods at the beginning of the system initialization, and utilize electrode readings (\(L(t+\tau)\)) as status labels. The indicators can be easily obtained by reading the registers, and the algorithms are feasible to be implemented on WSN sensor nodes.
* **Training a classifier:** We first train each node a specialized classifier at the base station. Though this way can predict future status accurately, we find that it consumes too many calculating resources and storage especially when the network has a large quantity of nodes. Fortunately, the indicators we selected may have similar patterns among nodes. For example, nodes deployed in the same bay will share the same tide period and may have similar wave exposures, and above water duration (\(D_{aw}\)) may thus also fit similar patterns among nodes. Utilizing this characteristic, we mix the data from all nodes and train a universal classifier.
* **Classifier dissemination:** Base station trains a universal classifier, and broadcasts it to each node. With the classifier and local system knowledge (\(D_{aw}(t),\ N_{nb}(t),\ N_{link}(t)\)), nodes can predict future status by itself accurately.
###### Abstract  
Accurate calibration of mesoscopic contact model parameters is essential for ensuring the reliability of Particle Flow Code in Three Dimensions (PFC3D) simulations in geotechnical engineering. Trial-and-error approaches are often used to determine the parameters of the contact model, but they are time-consuming, labor-intensive, and offer no guarantee of parameter validity or simulation credibility. Although conventional machine learning techniques have been applied to invert the contact model parameters, they are hampered by the difficulty of selecting the optimal hyperparameters and, in some cases, insufficient data, which limits both the predictive accuracy and robustness. In this study, a total of 361 PFC3D uniaxial compression simulations using a linear parallel bond model with varied mesoscopic parameters were generated to capture a wide range of rock and geotechnical material behaviors. From each stress-strain curve, eight characteristic points were extracted as inputs to a multi-objective Automated Machine Learning (AutoML) model designed to invert three key mesoscopic parameters, i.e., the elastic modulus (\(E\)), stiffness ratio (\(k_{d}/k_{n}\)), and degraded elastic modulus (\(E_{d}\)). The developed AutoML model, comprising two hidden layers of 256 and 32 neurons with ReLU activation function, achieved coefficients of determination (\(R^{2}\)) of 0.992, 0.710, and 0.521 for \(E\), \(k_{s}/k_{n}\), and \(E_{d}\), respectively, demonstrating acceptable predictive accuracy and generalizability. The multi-objective AutoML model was also applied to invert the parameters from three independent uniaxial compression tests on rock-like materials to validate its practical performance. The close match between the experimental and numerically simulated stress-strain curves confirmed the model's reliability for mesoscopic parameter inversion in PFC3D.  
mesoscopic parameter inversion; PFC3D simulations; automated machine learning (AutoML); uniaxial compression tests; linear parallel bond contact model 2025158181. [[https://doi.org/10.3390/app15158181](https://doi.org/10.3390/app15158181)]([https://doi.org/10.3390/app15158181](https://doi.org/10.3390/app15158181))  
1
## 3 Results
The results also demonstrated a significant enhancement in performance, with a 57% improvement observed in shifting eigen frequency to become higher compared to the non-optimized structure. Additionally, we achieved a substantial reduction in weight by 50%, resulting in a lighter structure. This weight reduction was achieved without compromising the desired vibration characteristics. We can conclude that the optimized design not only maintains the structural integrity but also enhances the dynamic performance, showcasing the effectiveness of the applied modifications. By integrating an eccentric mass, engineers can precisely modify the natural frequency of structures, thereby optimizing stability and performance. The structure depicted in Figure 6b features a pillar-like design with a square base, specifically configured for modifications aimed at altering its dynamic behavior. Figure 6a,c-f present various views of the original structure, including the side, front, and top perspectives, which collectively illustrate its geometric configuration and form the basis for subsequent modifications. Figure 7 illustrates the convergence pattern of our optimization algorithm over multiple iterations. The results demonstrate a significant performance enhancement, with a 64% increase in eigenfrequency relative to the non-optimized structure. Concurrently, a 50% reduction in weight was achieved, resulting in a lighter structure that maintains the desired vibration characteristics. These outcomes validate that the optimized design not only preserves structural integrity but also markedly improves dynamic performance, highlighting the effectiveness of the modifications in achieving superior structural efficiency and resilience. Both the central and eccentric mass cases demonstrate significant enhancements in dynamic performance and weight reduction. The central mass case emphasizes a strategic volume reduction for improved stability and material optimization, while the eccentric  
Figure 5: Eigen frequency history of the pillar design domain with central mass. mass case focuses on adjusting natural frequency for performance improvements. Both approaches achieve a 50% weight reduction and substantial increases in eigenfrequency, underscoring the effectiveness of the optimization techniques applied in each case.
# Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison
## 3 Simulation Results
### Tests under Steady-State Conditions
#### 3.2.1 Off-Nominal Frequency  
The first set of tests is focused on testing the accuracy achieved by the different algorithms when the input is sinusoidal, positive sequence, but the frequency differs from its rated value. In particular, different frequencies have been considered. Figure 5 reports the maximum TVE under steady-state conditions for all the algorithms; frequency spans the range \([48,52]\) Hz with a step of 0.2 Hz. Since the negative sequence input is zero, positive sequence synchrophaser estimates are not affected by spectral interference. In particular, TVE values are negligible for the SV-F and SV-IpDFT, since they provide a theoretically exact compensation of the scalloping loss, which is the only error source under these conditions. TVE is not negligible (albeit it is still very low) as far as the SV-TF (\(<\)7.1 \(\cdot\) 10\({}^{-3}\)) and IEC-P (\(<\)4.5 \(\cdot\) 10\({}^{-3}\)) methods are concerned. The reason is that the SV-TF technique does not compensate for scalloping loss due to the employed filters, while IEC-P uses an approximate formula, thus leading to its mitigation but not to a complete cancellation. All the algorithms result in negligible RFE values (in the order of 10\({}^{-8}\) Hz/s or even lower), while the \(|\)FE\(|\) has a meaningful value only for SV-TF (0.078 mHz at 48 Hz). In this case, the reason is undermodeling of the positive sequence synchrophaser because of its truncated Taylor expansion and the consequent error infiltration in (36), which is used for obtaining the frequency estimate. It is worth noting that both FE and TVE values can be reduced by obtaining the filter bank from a WLS solution (e.g., weights corresponding to the coefficients of a Hann window), as previously mentioned in Section 2.4.
# Data Structure and Management Protocol to Enhance Name Resolving in Named Data Networking
## 4 Experimental Setup and Results
### Use Case Scenario  
Let us start our scenario with the following assumption: (1) a producer named SQU with the NDN domain name \"/om/edu/squ\" is located in \"AS1\", (2) \"AS1\" and \"AS2\" administrators are running in their respective domains: \"/AS1/PNPS\", \"/AS2/PNPS\", (3) \"AS1\" and \"AS2\" are peered using N-BGP [2], and each domain is advertising its own PNPS name, and (4) \"C1\" and \"C2\" are three consumers located in \"AS1\" and \"AS2\", respectively.  
Based on the above assumptions, \"C1\" can reach SQU via static/intra-domain routing, whereas \"C2\" cannot reach the SQU domain name, as it is not globally advertised. Therefore, if C2 sends an NDN Interest to the SQU domain, it will receive an NDN Nack (Figure 7(a)). Additionally, if it sends GET/GETBCM to its domain PNPS, it will receive a nil result, since \"/AS2/PNPS\" does not have a mapping entry for the SQU domain name (Figure 7(b)).  
SQU decided to solve this issue by providing reachability to its domain for clients in \"AS2\". To provide reachability, the SQU network administrator uses PNPC to send an ADD message to \"/AS2/PNPS\", as follows:  
\[\text{pnpc}\leftarrow\text{server}=/\text{AS2}/\text{PNPS}-\text{message}= \text{add}-\text{name}=/\text{om/edu/squ}-\text{location}=/\text{AS1}\]  
Figure 7: Memory consumption while loading 1 million entries into an empty trie.  
Since \"/AS2/PNPS\" is globally advertised using N-BGP, the NDN Interest packet holding the ADD message can reach its destination. When PNPS of \"AS2\" receives the NDN Interest, it validates the Interest signature (outside the scope of this paper) against the name \"/om/edu/squ\". If the Interest packet is signed by an entity that is authorized to modify entries of this domain name, access will be granted, and the ECHT add operation will take place. Otherwise, a \"not authorized\" data message is sent back.  
Now, \"C2\" will still receive a Nack message when sending an NDN Interest to the SQU domain. However, \"C2\" can get the delivery location(s) of the SQU domain by sending a GET/GETBCM message to \"/AS2/PNPS\", which will be \"/AS1\". \"C2\" will use \"/AS1\" as a ForwardingHint [23] for a new Interest packet with the name element \"/om/edu/squ\". As \"/AS1\" is publicly advertised via N-BGP, \"AS2\" can now route the packet to its destination (Figure (c)c).
## 2 Materials and Methods
### Laboratory Setup
#### 2.1.4 Relays  
In order to develop a real-time FDII system, relays were designed to introduce multiplicative faults into the system by abruptly changing one of the amplifier parameters. Figure 3a shows the electronic schematic of the main part of the relay board used for connecting a resistance \(R_{i}\) (\(i=1,\,9,\,12\)) into the amplifier circuit.  
\begin{table}
\begin{tabular}{c c} \hline \hline Armature inductance & \(L_{\text{a}}=0.77\cdot 10^{-3}\) H \\ \multicolumn{1}{c}{Gear ratio} & \(N=3.8\) \\ Amplifier resistor \(R_{1}\) & \(R_{1}=1\)\(\Omega\) \\ Amplifier resistor \(R_{5}\) & \(R_{5}=1\) k\(\Omega\) \\ Amplifier resistors \(R_{9}\), \(R_{10}\), and \(R_{12}\) & \(R_{9}=R_{10}=R_{12}=10\) k\(\Omega\) \\ Amplifier resistors \(R_{14}\) and \(R_{15}\) & \(R_{14}=R_{15}=1\) k\(\Omega\) \\ Equivalent resistor \(R_{\text{e1}}\) & \(R_{\text{e1}}=R_{9}\cdot R_{10}/(R_{9}+R_{10})=5\) k\(\Omega\) \\ Equivalent resistor \(R_{\text{e2}}\) & \(R_{\text{e2}}=R_{12}+R_{14}+R_{15}=12\) k\(\Omega\) \\ Amplifier capacitor \(C_{8}\) & \(C_{8}=48\) pF \\ \hline \hline \end{tabular} \({}^{*}\) The total moment of inertia of the DC motor is expressed as the sum \(J=J_{m}+J_{gr}+J_{e}=1.43\cdot 10^{-6}\) kgm\({}^{2}\). \({}^{**}\) This values of \(B\) and \(M_{\text{c}}\) are not specified in the datasheet for the particular motor but were determined experimentally [45].  
\end{table}
Table 1: _Cont._  
Figure 3: Relay for switching resistance: (**a**) Electronic schematic; (**b**) Physical board.  
Figure 3b represents an example of a printed circuit board used to switch on or off resistance \(R_{1}\) in the amplifier circuit by assigning a binary value 0/1 to the corresponding digital outputs.  
When the value of the \(i\)-th digital output (D\({}_{\text{oi}}\))of the NI 9403 module is 0, the 2N3904 transistor is in the conducting state, and the relay's electromagnet shifts the contact from the NC (normally closed) position to the NO (normally open) position, thereby connecting the desired resistance \(R_{i}\) (\(i\) = 1, 9, 12) (or capacitance \(C_{8}\)) into the amplifier circuit.  
When the value of the digital output is 1, the bipolar transistor does not conduct, and no current flows through the relay coil. In this case, the NO contacts remain open, while the NC contacts are closed.  
There are four printed circuit boards (PCBs), and each can switch between four different resistance values \(R_{i}\) (\(i\) = 1, 9, 12), or four capacitance values \(C_{8}\) of the electronic amplifier, as specified in Table 2.
# Optimisation of Energy Transfer in Reluctance Coil Guns: Application to Soccer Ball Launchers
## 5 Results and Discussion  
### Discussion on Simulation Results  
#### 5.1.1 Impact of the Coil Gun Structure  
As presented in the results section, increasing the number of coils allows to transfer more power to the ball as shown in Table 2:  
Increasing the number of coils from one to two allows to increase the speed by 18%, corresponding to an energy transfer optimization of 40%. However, increasing the number of coils from two to three or four allows to increase the speed by only respectively 0.6% and 1.8%, corresponding to an energy transfer optimization of respectively 1.2% and 3.6%. This result is not intuitive and is important. Consequently, considering the impact of adding a coil to the EML in terms of mechanical and electrical integration, the two-coil EML seems to be the best configuration in our case.
## 5 Performance Evaluation
### Communication Overhead  
We assume the format of a packet is the same as that in TinyOS [26]. The timestamp occupies 128 bits. The sizes of prime numbers \(p\), and \(q\) needed in the Paillier encryption are 512 bits each. The size of elements in \(Z_{n}^{*}\) is 1024 bits. We further assume the plaintext data occupies 32 bits, then random from stream cipher occupies the same byte width with the plaintext data, and Paillier encryption occupies 4096 bits, while the hash function with timestamp occupies 256 bits.  
For simplicity, we denote \(\{|X|,|R|,|E|,|H|\}\) as the plaintext data size, masking random key (noise) size, Paillier encryption size, and the size of hash function random.
## 2 Samples and Experimental Methods
### CO\({}_{2}\) & N\({}_{2}\) Isotherm Adsorption  
According to the IUPAC classification scheme, the pores size classification is divided into micropores (<2 nm), mesopores (2-50 nm), and macropores (>50 nm) in our paper [28]. And N\({}_{2}\) and CO\({}_{2}\) isothermal adsorption can be used to study the pore structure of shale and obtain parameters such as specific surface area and pore volume. Specifically, CO\({}_{2}\) can be used to test the pore parameters of micropores less than 2 nm in length according to the DFT (density functional theory) model [29], and the data were measured at 273 K with 0.0003 to 0.03 relative pressure (P/P\({}_{0}\), tested adsorption pressure/saturated pressure). In contrast, N\({}_{2}\) adsorption is mainly used to interpret the pore parameters of 2-200 nm size according to the BJH (Barrett-Joyner-Halenda) model [30], and the tests were performed at 77 K ranging from 0.001 to 0.999 relative pressure.  
Both N\({}_{2}\) and CO\({}_{2}\) isothermal adsorption were tested in a Micromerities ASAP 2020 (Norcross, GA, USA). Twelve marine and continental shale samples were ground into 60-80 mesh to ensure that sufficient pore information is retained and the adsorption gas can fully contact the samples. First, 0.5 g of the samples were put into a vacuum heater at 150 \({}^{\circ}\)C for 24 h to remove moisture and other gases. Subsequently, the adsorbed gas will be gradually injected into the sample test tube. The adsorption pressure will slowly increase with time until it equals the saturated pressure, and then the samples enter the desorption branch, which will take about 16 h. The software will automatically record the difference between adsorption and desorption branches under different relative pressures, which also represents the hysteresis loop because of the principle of capillary agglomeration. The data was processed by the ASAP 2020 Plus Physi software. And the detailed introduction of the CO\({}_{2}\) and N\({}_{2}\) isotherm adsorption method has been explicitly reported by previous studies [15; 31; 32].