esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Analysis
BAM exhibits the highest performance for MNLI and RTE. MTDNN presents the highest performance for STS-B, and SMART for MNLI and CoLA. Our model--using the SIWLM scheme--exhibits the highest performance for QQP, QNLI, SST-2, and MRPC. We compare the performance of our model to MT-DNN and SMART, our baseline models. As depicted in Table 4, the best result for each task is in bold font, and the second-best result is underlined. MTDNN-SIWLM outperforms MT-DNN and SMART in six tasks (MNLI, QQP, QNLI, SST-2, MRPC, and RTE) than MT-DNN and SMART. Moreover, it has the second-best performance in CoLA and STS-B. MTDNN-SIWLM achieves accuracies of 84.8% and 84.0% in MNLI-m and MNLI-mm, outperforming SMART by 0.1% and 0.8%. QQP and SST-2, respectively, present improvements of 0.1% for F1 score and 0.7% for accuracy compared with SMART. STS-B exhibits improved performance for both evaluation indexes, with increases of 1.25% and 1.3% for Pearson and Spearman, respectively. MRPC and RTE, with small data sizes, demonstrate accuracy improvements of 4.1% and 5.6%.
## 2 Related Work
### Reconstruction Time-Related  
NeRF can generate high-resolution images with a small spatial footprint. However, the original NeRF used an MLP-based neural network representation, resulting in slow reconstruction and inference speeds, which is one of the limiting factors in its practical application. Several methods have emerged in recent years aimed at improving NeRF's reconstruction efficiency.  
EfficientNeRF [27] employs effective sampling and a new data structure to accelerate NeRF's training and inference processes, reducing training time by more than 88%. However, it still requires several hours of training. Plenoxels [28] adopts an approach based on sparse voxel grids and spherical harmonics, eliminating the need for neural networks. This method achieves faster training speeds than the baseline NeRF model with comparable quality. However, it necessitates manual parameter adjustments depending on the specific scene. Both DVGO [29] and TensorRF [30] optimize directly on voxel grids. DVGO, which optimizes voxel grids directly, achieves a training speed 45 times faster than the baseline NeRF but struggles with scenes without boundaries or forward-facing scenes. TensorRF employs various tensor decomposition techniques to reduce memory usage and achieve efficient rendering, though it only supports bounded scenes. Moreover, Instant-ngp [31] utilizes multi-resolution hash encoding techniques, significantly reducing the computational cost of representing high-resolution image features and cutting training times down to just a few seconds from minutes. These methods have played a vital role in advancing implicit representation reconstruction. However, they all rely on precise pose information.  
Furthermore, some research focuses on improving reconstruction efficiency based on joint pose optimization methods. NeRFac [32] builds on BARF, introducing an occupancy grid that accelerates BARF by skipping empty areas. In research concurrent with our method, BAA-NGP [33] attempts to integrate multi-resolution hash encoding into the joint optimization framework to speed up scene convergence. However, BAA-NGP needs to clearly elucidate the intrinsic relationship between multi-resolution hash encoding and pose estimation, necessitating further discussion.  
In summary, studies on joint pose optimization still suffer from the problem of low reconstruction efficiency. To address this, we have incorporated multi-resolution hash encoding into the joint pose optimization method and explored the relationship between pose estimation and hash encoding at different resolutions. A series of subsequent experiments have provided theoretical and experimental evidence for the improved approach presented in this paper.
## 2 Results
### Ultra-High Sensitivity Quantitative Phosphoproteomic Analysis
\begin{table}
\begin{tabular}{c c c c} \hline CF170 & KWMHAHYSR & S8:Phospho & \(3.21\times 10^{-6}\) \\ CI169 & NPYAHISIPR & S7:Phospho & \(0.003453563\) \\ CP250 & QESISSELITLLR & S2:Phospho & \(0.00612397\) \\ CSPP1 & QPSPIVPALQNK & S3:Phospho & \(0.004669614\) \\ EPS8 & APAPAPGPGTVTQVDVR & T9:Phospho & \(0.000581478\) \\ EPS8 & KGPGEGVTLLR & T9:Phospho & \(0.00052707\) \\ ESSL1 & AAGEGLLTLR & T8:Phospho & \(1.84\times 10^{-5}\) \\ ESSL1 & SLNSTPPPPAPAPAPPPALAR & S4:Phospho & \(0.014008085\) \\ ESSL2 & HSPTSEPTPPGDALPPVSSPHTH & S19:Phospho & \(0.040008069\) \\ EZRI & QLLTLSSELSQAR & T4:Phospho & \(8.73\times 10^{-6}\) \\ GPC5C & GVGYETILK & T6:Phospho & \(1.68\times 10^{-8}\) \\ GPC5C & VPSEGAYDIILR & S3:Phospho & \(0.000326668\) \\ GSK3B & GEPNVSYICSR & Y7:Phospho & \(0.008989048\) \\ HSPB1 & GPSWDPFR & S3:Phospho & \(0.047463946\) \\ ICK & STPGLIPRPPAAQPVHGR & T2:Phospho & \(0.006484348\) \\ LDHA & TPKIVSGK & T1:Phospho & \(0.020666271\) \\ MUC1 & DTYHPMSEPTYTHTHGR & T11:Phospho & \(0.025694245\) \\ RAB10 & FHTITSYYR & T3:Phospho & \(0.005397277\) \\ SARG & ANSALTPPKPESGLTLQESTTGL & RT6:Phospho & \(0.041546421\) \\ SH56L & TPNLDWR & T1:Phospho & \(0.003375752\) \\ TRRAP & LEPAFLSGLR & S7:Phospho & \(0.043482561\) \\ YG015 & IELSYNK & Y5:Phospho & \(0.001729708\) \\ \hline \end{tabular} (f)  
\begin{tabular}{c c c} \hline AMNLS & RLSLVPK & S3:Phospho & \(0.014008085\) \\ BAIP2 & AFFAQTASGFK & T6:Phospho & \(0.00053512\) \\ C2D1A & GPASTPTYSPAPTQPAP & S9:Phospho & \(1.85\times 10^{-11}\) \\ DYRK2 & KPSAAAPAAYPTGR & S3:Phospho & \(0.001809625\) \\ G3P & GALQNIIPASTGAAK & S10:Phospho & \(0.022036915\) \\ GPC5B & TAGFPNGSLGK & S8:Phospho & \(0.011034256\) \\ IF4E2 & TASDQATTARIR & S3:Phospho & \(0.012911956\) \\ IST1 & NISSAQUVGCPKPPEASAK & S3:Phospho & \(2.28\times 10^{-5}\) \\ K1522 & ASPVPAPSSGLHAAVR & S2:Phospho & \(0.017621205\) \\ KIF12 & DLLSLGSPR & S7:Phospho & \(0.000979657\) \\ KIF12 & SPGQVLPPH & S1:Phospho & \(1.17\times 10^{-10}\) \\ LMNA & SGAQASSTPLSPTRITR & S11:Phospho & \(0.033756475\) \\ MAP1A & TLPQEPGK & T1:Phospho & \(0.037174836\) \\ MILK1 & SPVPSPGSSSPQLQVK & S10:Phospho & \(0.002110621\) \\ MY15B & AVPSPPPPPIVK & S4:Phospho & \(0.02828527\) \\ NPT2A & LGSPAVSPLPVR & S7:Phospho & \(0.00017686\) \\ SL9A3 & RGSLAFIR & S3:Phospho & \(0.031611131\) \\ TRPV5 & ASLALPTSSLSR & S11:Phospho & \(0.000897584\) \\ VP37D & SAQPATSADPPK & S1:Phospho & \(0.004954683\) \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._with DN and DM are not a result of changes in protein expression and that phosphorylated proteins are more specific to DN patients (Figure 3). The results also validated our strategy to indicate disease progression through changes in phosphorylated proteins rather than changes in total protein expression.
# Research on Monocular-Vision-Based Finger-Joint-Angle-Measurement System
## 2 Biological Structure of Human Fingers and Their Movement Characteristics  
### Structural Composition of the Human Hand  
The human hand consists of the index finger (IF), middle finger (MF), ring finger (RF), little finger (LF), and thumb (TUM). The IF, MF, RF, and LF consist of one degree of freedom (DOF) distal phalangeal (DIP), one DOF proximal phalangeal (PIP), and two-DOF metacarpophalangeal (MCP) and two-DOF carpometacarpal (CMC) joints, respectively. The thumb consists of a one-DOF distal phalangeal joint (IP), a two-DOF metacarpophalangeal joint (MCP), and a two-DOF carpometacarpal joint (TM) [21], as shown in Figure 1.  
Figure 1: Structural components of the human hand.
## 3 Cooperative Perception Information-Sharing
### Cooperative Perception Information-Sharing Network  
Currently, there are two main V2X communication technologies, including dedicated short-range communication (DSRC) and cellular vehicle-to-everything (C-V2X) [59]. Among them, DSRC [60] is specifically used for V2V and V2I communication. It has the advantages of a high transmission rate, low latency, and support for point-to-point and point-to-multipoint communication. C-V2X communication, represented by 5G, has the advantages of wide coverage, high reliability, and large data capacity. It is suitable for vehicle-road coordination and communication between edge servers. These two communication modes jointly support the diverse application requirements of the IoV environment.
## 2 Materials and Methods  
The code for the models was prepared and executed within a Python 3.10.12 environment using sklearn 1.2.2 and keras 2.14.0. A 16 GB NVIDIA T4 Tensor Core GPU was used, and the CRISP-DM process was followed, with the phases of data understanding, data preparation, modeling, and evaluation [19]. The methods described below primarily use these libraries for the models and for the preparation of the text, with the assistance of numpy, pandas, and other common multi-task Python libraries. Many NLP libraries such as NLTK, spaCy, and word2 vec were not used as they were created for Western languages; applying them to non-Western languages can lead to unintended results. An additional library used for the paper was the jieba library, which was created to segment Chinese text into \"words\" for NLP tasks due to the Chinese language's lack of space separation in writing [20]. The BERT Chinese-language tokenizer _bert-base-chinese_ was used in the BERT models.  
Four algorithms were evaluated for their performance on this dataset: Naive Bayes, XGBClassifier, BERT, and neural network (sequential). For the first three approaches, an 85%/15% train/holdout split was used to monitor overfitting since there are many data points available. For the NN-sequential approach, a 70%/15%/15% train/validate/holdout split was used where the model is trained with the train set, the validation set is used for making decisions about hyperparameters, and the test set is used to evaluate a few selected models with the best performance. A stratified split is used to make sure minority classes are represented due to the class imbalance noted in Figure 1.  
Other methods described below include the NLP processing techniques used, the six iterations of data preparation that were evaluated in this work, and details related to the four families of algorithms that were applied to this dataset.
## 2 Related Work
Works [10; 23] show the expediency of recognizing spoofing attacks based on the analysis of anomalies in the incoming video stream and the dynamics of emotions manifested on a person's face. Since the problem of face recognition by FIs and IEs belongs to the class of problems of neural network image analysis, in most known solutions, one of the proven types of convolutional neural networks (CNNs) is used as a basis, which ensures the achievement of sufficiently high accuracy and speed recognition. At the same time, the known NNMs used for BA do not fully take into account modern advances in the field of semantic image segmentation, which can be used in the pre-processing of the input image to highlight FI and IE borders. Moreover, the specifics of various CIFs, related to the efficiency of updating the CIF personnel data set, impose restrictions that are difficult to ensure in a CNN built on the basis of an image classification approach or an image comparison approach, which can be compensated for by using a modular NNM that includes modules with appropriate functionality or by using end-to-end deep learning models. However, the features of end-to-end deep learning models related to the need to retrain the entire model in the event of a modification in the training sample, for example, due to the need to detect new types of interference or due to the need to analyze new parameters of spoofing attacks, significantly complicate the operational adaptation of BA tools to variable application conditions. In addition, relative to end-to-end deep learning models, modular NNMs are considered more productive and computationally efficient due to their ability to distribute computational loads between individual modules and to optimize the use of hardware resources by adapting each module to a specific subtask. Thus, the goal of this scientific research is the development of a modular neural network model that provides effective authentication based on images of the face and the iris of the eye at a critical infrastructure facility, taking into account the need to recognize spoofing attacks and promptly update data on the list of legitimate personnel representatives of the object.
# Implementation of a Topology Independent MAC (TiMAC) Policy on a Low-Cost IoT System
###### Abstract  
The emerging new paradigm under the fifth generation of wireless communications technologies (5G) and high expectations for massively expanding today's Internet of Things (IoT) under 5G, are expected to support a large plurality of low-cost devices for an all-increasing number of new IoT applications. Many emerging IoT applications are going to take advantage of techniques and technologies that have high demands from low-cost devices in terms of processing large amounts of data and communication. For example, in systems based on fog computing technology, low-cost devices have to assign some of their limited resources for processing purposes. Considering the drawbacks emerging from using low-cost devices and the fact that many applications are in need for time-constrained approaches, TDMA-based _Medium Access Control_ (MAC) policies need to be revisited and implemented in low-cost devices of today. In this sense, a policy independent of the underlying topology, TiMAC policy, is considered here and is implemented in low-cost devices using 433 MHz RF modules. Even though the implementation is limited by synchronization issues and a small number of nodes, the obtained experimental results demonstrate the potential for employing TDMA-based MAC policies on IoT systems consisting of low-cost devices.  
implementation; IoT; low-cost; time constraints; medium access control; topology independent 2020
## 2 Materials and Methods
### Convolutional Neural Network Backbones Used in This Work
#### 2.3.5 SqueezeNet  
In terms of model complexity, SqueezeNet [30] is the smallest CNN model employed in this study. It was developed based on AlexNet with 50 fewer parameters by reducing the size of the filter and still maintaining high accuracy.
# Smart Grid Testing Management Platform (SGTMP) +
## 4 SGTMP Deployment Scenario--SG Component Stress Testing
### Simulation Results Overview  
The user can perform all of the required tasks using a web browser in the SGTMP GUI. These include creating and editing tests along with defining their test pass criteria and the SG topology. All test definitions can be viewed and modified. From there, individual tests can be started, and their progress examined. The final results can be observed, which provides an overview of all the executed tests including their pass/fail status (Figure 12). A more detailed view, as shown in Figure 13, is also available for each test run, which contains detailed logs collected from each SGTMP node during the simulation. This data can be used to run further analysis outside of SGTMP, such as aggregation and analysis of multiple test runs using tools which are suitable for the user. This data is in JSON format to simplify the analysis of recorded data.  
From the collected data, we showcase some of the results created from the observed measures. One example is testing the performance and reliability of the SMDC component under changing network conditions. For this experiment, we used a virtualized SM featuring only the necessary network communication capabilities. These SMs were then requested by SGTMP to send out their payload at the **same time** to the SMDC, to create a burst traffic load on the network and SMDC. The goal was to determine the behavior (response time per request and reliability) of the SMDC in the worst case scenario in which all SMs send data at once. Figure 14 shows the behavior of a single SMDC under an increasing number of burst data transmitted. Such increase in requests can be defined according to the needs of the tests.  
Figure 12: Overview of test runs for a given test definition.  
Figure 13: Details of a test run including simulator view and observed measures for each simulator.  
As another example, to test the SMDC under sustained load, we used two hardware SMs connected to a single SMDC. SGTMP instructed each of the SMs to send requests to the SMDC in constant intervals, distributing the requests in a round-robin way between the two SMs. The results of an SMDC in more realistic continuous load are shown in Figure 15.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 3 Limitation and Advantages of Underwater Communication Technology  
In this section, the advantages and disadvantages of different communication technologies underwater such as attenuation, speed, data rate, distance, etc. are described. Tables 1 and 2 show a comparison between different communication technologies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Acoustic** & **RF** & **Optical** \\ \hline Attenuation & Distance and frequency band & Frequency dependent on & 0.39 dB/m (in ocean) \\ underwater & (0.1–4 dB/km) & (3.4–5 dB/m) & 11 dB/m (in turbid water) \\ \hline Speed of signal & \(\approx\)1500 ms\({}^{-1}\) & \(2.3\times 10^{8}\) ms\({}^{-1}\) & \(2.3\times 10^{8}\) ms\({}^{-1}\) \\ \hline Data transfer rate & \(\approx\)kbps & \(\approx\)Mbps & \(\approx\)Gbps \\ \hline Delay in & High & Moderate & Low \\ communication & & & \\ \hline Approximate & \(\approx\)km & \(<\)10.5 m & \(\approx\)10–99 m \\ Distance & & & \\ \hline Bandwidth & 1–100 kHz & \(\approx\)MHz & \(\leq\)150 MHz \\ \hline Frequency band & 10–15 kHz & 30–300 Hz & \(\approx\)5 \(\times\) 1014 Hz \\ \hline Transmission & \(>\)10 W & mW–W & mW–W \\ power & & & \\ \hline Antenna size & 0.1 m & 0.5 m & 0.1 m \\ \hline Performance & Temperature, pressure and & Conductivity & Absorption and turbidity \\ parameter & salinity & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different communication technologies in underwater.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **MI** & **Low Frequency** & **IR** \\ \hline Attenuation & 30–300 kHz & 3–30 kHz & Depends on the distance \\ Speed(m/s) & \(3\times 10^{8}\) m/s & - & - \\ Data rate & \(\sim\)kbps & \(\sim\)Hundreds bps & \(\sim\)Gbps \\ Latency & Very low & Very low & Low \\ Distance & \(\approx\)10 m & Up to hundreds Kms & \(\approx\)3 m \\ Bandwidth & - & - & MHz \\ Frequency band & - & - & - \\ Transmission power & \(10^{-8}\) W & - & - \\ Antenna size & 0.1m & - & - \\ Performance parameter &
\begin{tabular}{c} Temperature, pressure \\ and salinity \\ \end{tabular} & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different communication technologies in underwater.
# Numerical Study on the Phase Sensitivity Variation in Low Frequency Primary Microphone Calibrations
## 2 Independent Pistonphone Simulation
### Results
#### 2.2.2 Results in Time Domain  
The transient analysis is performed at frequencies of 1 Hz, 0.1 Hz, 0.01 Hz, and 0.001 Hz. The sound pressure waveforms in two cycles are obtained as shown in Figure 6.  
Apparently, the sound pressure in the ideal sealing and adiabatic conditions is synchronized with the piston displacement, and thus its waveform can also characterize the waveform of the piston displacement. Then, the phase difference between the sound pressure and the piston displacement in other conditions can be obtained by calculating the time difference of its second peak with that of the sealing and adiabatic condition. The first peak of the waveform is only a transition state and is not selected.  
It can be seen that the pressure leakage and heat conduction effects will cause the pressure attenuation, and the phase of the sound pressure will be ahead of the piston displacement rather than lagging behind. The greater the pressure decrease, the greater the phase leading. The angles of the phase lead are calculated and marked in the legends, and the results from the transient analysis are coincident with the frequency domain analysis.  
Let us analyze the first three quarters of the first period of the leaking and heat conduction model at 0.001 Hz, as shown in Figure 6d. When the piston sinusoidally moves to its positive extreme position, the increase in the generated sound pressure slows down, and the severe pressure leakage effect causes it to decrease to 0. When the piston moves from this positive extreme position to the negative position, the air would all along be expanded from zero to negative pressure and would have no relation to the equilibrium position of the piston. Therefore, the phase lead of the calibrating sound pressure to the piston displacement occurs.  
Figure 6: (**a**) Sound pressure waveforms of the calibrating sound pressure at 1 Hz. (**b**) Sound pressure waveforms of the calibrating sound pressure at 0.1 Hz. (**c**) Sound pressure waveforms of the calibrating sound pressure at 0.01 Hz. (**d**) Sound pressure waveforms of the calibrating sound pressure at 0.001 Hz.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 3 System Model
### Channel Model
#### 3.2.2 Ambient Noise  
The ambient noise of underwater by the empirical formula is described as below [33].  
\[N(f)=N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f) \tag{3}\]  
where \(N_{t}(f)\), \(N_{s}(f)\), \(N_{w}(f)\), \(N_{th}(f)\) denote the power spectral density (p.s.d) of turbulence, shipping, waves, and thermal noise, respectively. Empirical formulas for these p.s.d in dB re \(\upmu\)Pa per Hz are given as [33]:  
\[\begin{array}{l}10\text{lg}N_{t}(f)=17-30\text{lg}f\\ 10\text{lg}N_{s}(f)=40+20(s-0.5)+26\text{lg}f-60\text{lg}(f+0.03)\\ 10\text{lg}N_{w}(f)=50+7.5w^{1/2}+20\text{lg}f-40\text{lg}(f+0.4)\\ 10\text{lg}N_{th}(f)=-15+20\text{lg}f\end{array} \tag{4}\]  
where the \(s\) is the shipping activity factor ranging between 0 and 1; at the same time, the \(w\) indicates the wind speed in m/s.