esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture and Key Technologies
### CMMN-Based Heterogeneous Cloud Resource Integration and Scheduling
#### 3.1.2 Task Scheduling Optimization Based on CMMN Algorithm  
In heterogeneous cloud task scheduling, traditional algorithms like Min-Min, Max-Min, Cloud List Scheduling (CLS), and Cloud Min-Min Scheduling (CMMS) are widely used but have limitations in resource allocation efficiency and flexibility. Min-Min and Max-Min focus on minimizing task completion time but fail to address load imbalance and resource utilization. At the same time, CLS and CMMS lack adaptability to dynamic resource and priority changes. The Cloud Min-Max Normalized (CMMN) strategy proposed by [PERSON] and [PERSON] (2019) [22] improves resource utilization, minimizes completion time, and struggles with exceptional tasks and task dependencies. We enhanced the CMMN algorithm by optimizing task dependencies and adjusting resource allocation to address these gaps. The improved algorithm handles complex scheduling in heterogeneous cloud environments, increasing resource efficiency and task flexibility. The CMMN algorithm, outlined in Algorithm 1, begins by iterating through the current task set (Line 1) and checking if each task's prerequisites are met (Line 2). For tasks meeting the requirements, their ready times and execution times across cloud platforms are updated (Lines 3-5) and added to the ready-to-schedule task set (Line 6) while being removed from the original task set (Lines 7-8). If the ready-to-schedule set is not empty (Line 9), the minimum and maximum values are calculated (Lines 10-11), and tasks are normalized based on their maximum values (Line 13). Tasks exceeding a user-defined  
Figure 2: Heterogeneous cloud resource integration framework. threshold are classified as large tasks (Lines 14-15) and small tasks (Lines 16-17). Finally, the Min-Min algorithm is applied to schedule the extensive (Line 18) and small task sets (Line 19). The original CMMN algorithm uses extreme values from the ETC matrix for task classification, which can misclassify small tasks into large task batches during the cloud readiness phase, causing load imbalance. Therefore, we replaced extreme values with the median (Lines \"10, \"11, and \"13), ensuring more accurate task classification and optimized load distribution. Additionally, the algorithm lacked consideration of predecessor task scheduling effects on successors. We introduced a correction step (Lines \"3-\"5) to adjust successor task completion times based on predecessor scheduling. This enhances task dependency handling, improves resource utilization, and reduces completion time. ``` Input: tasks-A list of tasks with prerequisites, arrival times, and execution times; resources-A list of resources, i.e. a list of clouds; completedTasks-A list to record the completion of tasks; threshold-Threshold for distinguishing between large and small tasks.
# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 5 Measurement Results
### Comparison with Other Channel Estimation Methods  
Most studies reported in the literature focus on improving the quality of channel estimation methods. The performance of these methods is typically evaluated using mean squared error (\(MSE\)) or bit error rate (BER) as functions of SNR. These indicators can be evaluated under the condition that the true values of channel coefficients and bits over time are known. Although it is not a problem for simulation research, this information is not available when capturing and analyzing live signals, as in this case. However, it is possible to obtain a rough estimate of \(MSE\). The \(MSE\) of channel estimation is calculated as follows [34]:  
\[MSE=\mathbb{E}\{(\mathbf{h}-\hat{\mathbf{h}})^{*}(\mathbf{h}-\hat{\mathbf{h}} )\}, \tag{7}\]  
where \(\mathbf{h}\) and \(\hat{\mathbf{h}}\) correspond, respectively, to the vectors of true and estimated channel coefficients, and \({}^{*}\) denotes the complex conjugate. Although the elements of \(\mathbf{h}\) are not known, they can be estimated based on the restored values of transmitted resource elements:  
\[\mathbf{h}^{\prime}=\frac{\mathbf{y_{i}}}{\mathbf{x_{i}}}, \tag{8}\]  
where \(\mathbf{x_{i}}\) and \(\mathbf{y_{i}}\) correspond to the vectors of true (restored) resource element values and received resource element values, respectively. The \(\mathbf{h}^{\prime}\) can be seen as an estimate of the channel coefficient, which is influenced by errors caused not only by the AWGN, but also by hardware-related factors such as phase noise, I/Q impairments, frequency offset, sampling offset, and others.  
Table 3 presents the MSE values for MLT and TIFA methods in the fading channel. These values are similar to the values obtained for the conventional LS approach in simulation research, as shown in [6; 14; 16; 18; 20; 22]. This shows that using a simplified channel coefficient population scheme does not negatively affect the overall SNR performance of the channel estimation procedure.
# Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications
## 2 Related Works  
Beam management and selection, a fundamental aspect of IRS-assisted mmWave V2X systems, have been at the forefront of investigation. However, efficiently managing the beams in such systems, especially coordinating between the BS and IRS for two-hop transmission, is challenging. Several papers have contributed novel approaches to this domain, enhancing the efficiency of mmWave V2X communication. A comprehensive survey by [10] offers a broad overview of IRS applications in vehicular communications, both ground-based and aerial, highlighting the potential and challenges of IRS-aided 6G vehicular communications. Additionally, ref. [11] discusses various use case scenarios for IRS-enabled V2X communications, particularly focusing on vehicular edge computing and IRS-enabled drone communications to minimize vehicle computational time.  
A machine learning-based approach proposed in [12] includes two phases: situational and mobility awareness. In the situational awareness phase, a model at the BS predicts the optimal IRS setup for a UE's location. The mobility awareness phase, at the UE, predicts UE mobility. This allows for predicting the optimal IRS configuration, and significantly reduces system overhead during initial access. In highly dynamic scenarios, it achieves higher spectral efficiency by using predicted UE locations. Leveraging location information, ref. [13] examines the impact of prior location data on channel parameter estimation in IRS-aided mmWave MIMO (Multiple-Input Multiple-Output) systems. It presents directional training beams based on rough location data and employs atomic norm minimization for channel parameter extraction. The authors of [14] introduce real-time software-controlled IRSs and examine their optimal placement in wireless networks, addressing reliability requirements for V2X communications. Furthermore, ref. [15] introduces a deep reinforcement learning algorithm for beamforming optimization in mmWave vehicle-to-infrastructure (V2I) communication systems. This novel algorithm tackles the non-convex and time-varying nature of joint beamforming matrix optimization for the BS and the IRS. The results demonstrate substantial network performance improvement, especially in dense V2I network scenarios.  
One approach, as proposed in [16], introduces multi-beam training methods for IRS-assisted multiuser communication, designed to reduce training time without compromising passive beamforming performance. It achieves this by partitioning IRS elements into sub-arrays and implementing a multi-beam codebook, thus enabling efficient beam direction control for different users. Ref. [17] explores the use of IRSs in a millimeter-wave vehicular communication network, introducing schemes to reduce channel estimation overhead and considering rate maximization in the uplink. Lastly, the study in [18] employs IRSs to mitigate Inter-Cell Interference (ICI) by using graph theory to minimize beam collisions.
## 2 Related Work
### Reinforcement Learning-Based Broadcasting Approaches  
Reinforcement learning (RL) approaches can be efficiently used to enhance broadcasting mechanisms' adaptability by allowing for nodes to learn from their past actions and the resulting network performance [15; 16]. In RL-driven schemes, each node iteratively adjusts its broadcasting behavior based on local or global feedback, often expressed as a \"reward\" or \"penalty\" [17]. For example, a reward may be provided when a node's decision leads to better coverage or reduced collisions. In contrast, a penalty is incurred for redundant transmissions or packet losses. In addition, the RL approach can be used in different steps of the mechanism, such as the decision-making process or in some steps of the pre-learning phase, in order to train the module.  
Q-learning and SARSA are among the most popular RL algorithms integrated into MANET broadcasting [18; 19]. In addition, fuzzy logic with double Q-learning for routing in intermittently connected mobile networks has also been proposed for a similar purpose [20; 21]. These algorithms update a value function over time, allowing for the node to converge toward an optimal or near-optimal decision policy for forwarding packets. While such RL-based methods can dynamically adapt to changing network conditions, they can also introduce computational overhead and convergence delays, particularly when the learning rate is slow or the state space is large. Consequently, the iterative nature of RL poses a trade-off between adaptability and computational cost, highlighting the need for efficient state representations and reward designs. In addition, in our previous work [1], we found that making dynamic membership in RL, while effective, can still lead to room for improvement when it comes to choosing the impact parameters necessary to further enhance the adaptability mentioned and the ability of networks to choose which parameters are most important for their function.
# A Hand Motor Skills Rehabilitation for the Injured Implemented on a Social Robot
## 3 Architecture of HaReS, the Hand Rehabilitation System
### sEMG Sensor  
Electromyography (EMG) is a technique used in clinical processes to study the electrical activity produced by skeletal muscles. The signals are captured by a series of electrodes that are placed in the muscle's belly. Surface EMG (sEMG) is a non-invasive type of EMG, and the electrodes should be positioned on the skin surface over the muscles to record the signals. Although this superficial type of EMG is less accurate than intramuscular EMG (invasive type), it is a better fit for our purpose. sEMG electrodes are user-friendly and they are accurate enough to capture muscle activity during exercise.  
Figure 1: Architecture of HaReS, a hand rehabilitation system, featuring surface electromyography (sEMG) and hand tracking low-cost sensors.  
The sensor of choice for HaReS end based on the BITalino project. This device aims to provide a framework for science education, research, and prototyping. It includes several sensors and actuators such as electrocardiogram (ECG), electrodermal activity (EDA), electroencephalogram (EEG) and, of course, electromyogram (EMG). The sEMG sensor of the BITalino is bipolar, which means that it has two electrodes for sensing the electrical activity of the muscle and one as a reference. The measuring range is \(\pm\)1.64 mV and the sampling rate is up to 4000 Hz.  
As suggested in [55], the optimal sample rate for EMG pattern recognition is between 400 and 500 Hz, so we used 500 Hz as the sampling rate for the data acquisition process that involved the BITalino sensor. To study the finger and wrist movements, the electrodes were placed on the flexor carpi radialis. In addition to the electrodes' placement, we were able to receive data from contraction of the flexor digitorum, and for the reference, we placed an electrode on the elbow bone.  
This sensor is used by HaReS to record the sEMG activity of a desired muscle. Given these data, along with the synchronized motion data captured by a hand-tracking sensor, an experienced therapist could detect if the patient is triggering the correct muscles during the performance of a certain task, thus having more insight about whether the rehabilitation program is improving the patient's skills. Figure 2 shows some sample hand poses with the corresponding sEMG data.
# Depth Image Completion through Iterative Low-Pass Filtering
## 3 Experimental Results  
In this section, we present a comprehensive overview of the experiments conducted to validate the proposed depth-completion methodology. Employing Kinect cameras, specifically Kinect for Xbox 360 and Kinect for Xbox One, RGB and depth images were captured to assess the approach's performance under diverse conditions. Notably, Kinect for Xbox 360 utilizes the structured light technique, while Kinect for Xbox One utilizes the time-of-flight technique.  
This study investigated the maximum defective area rate, estimated the continuous defective area, and evaluated the denoising capability of the Depth Image Completion Process (DICP) for a real-scene depth camera. Through a series of tests with a real-scene captured using Kinect for Xbox One, the achieved precision is discussed concerning the extent of the defective area. Additionally, we ascertain the maximum denoising ability and evaluate the overall performance of our method. The denoising ability test simulates common noise encountered in RGB-D cameras, creating rigorous noise conditions by manually removing continuous depth data and introducing normally distributed noise.  
For a comprehensive assessment of the results, this study calculated the accuracy and precision of each depth matrix. Accuracy error (ACC) and precision error (PRS) were defined and employed for quantitative evaluation, with the definitions as follows [39]:  
\[\text{ACC}=\frac{1}{N}\sum P_{fit_{i}}-\frac{1}{N}P_{raw_{i}}, \tag{3}\]  
\[PRS=\sqrt{\frac{1}{N}[\sum\left(P_{fit_{i}}-P_{raw_{i}}\right)-\left(\frac{1} {N}\sum P_{fit_{i}}-\frac{1}{N}P_{raw_{i}}\right)]^{2}}, \tag{4}\]  
where \(P_{fit_{i}}\) is the depth matrix generated from the DICP; \(P_{raw_{i}}\) is the raw depth matrix; and \(N\) is the total number of pixels inside the operation block.  
We also present benchmark results, comparing our method with advanced approaches [24,28], utilizing the NYU Depth V2 dataset [39]. The evaluation metrics included the Root Mean  
Figure 3: (**a**) L1 norm & Iterations analysis. (**b**) MAE & Iterations analysis.  
Square Error (RMSE), Mean Absolute Error (MAE), and Absolute Relative Error (Rel), following established methodologies [28; 39; 40]. The definitions for these evaluation metrics are provided below:  
\[RMSE=\sqrt{\frac{1}{N}}{\sum\left({{P_{fit_{i}}}-{P_{rawi}}}\right)^{2}}, \tag{5}\]  
\[MAE=\frac{1}{N}{\sum\left|{{P_{fit_{i}}}-{P_{rawi}}}\right|}, \tag{6}\]  
\[Rel=\frac{1}{N}{\sum\left|{\frac{{{P_{fit_{i}}}-{P_{rawi}}}}{{{P_{rawi}}}}} \right|} \tag{7}\]
## 4 Experimental Setup  
Figure 6 presents the experimental setup of an underwater VLC system with CAP modulation. At the transmitter end, the original data are mapped to 8 QAM complex symbols. Then, the symbols are up-sampled with an up-sampling factor of 4. The complex up-sampled symbols are separated to in-phase (I) and quadrature (Q) and filtered by a pair of pulse-shaping filters. After this, the digital signal is converted to an analog signal using a 4.2 GSa/s arbitrary waveform generator (AWG, Tektronix AWG710B). The sampling rate of the AWG can be set according to specific experimental needs. The generated analog signal is first pre-equalized by a circuit [35] and amplified by a 1 GHz electrical amplifier (EA, Mini-Circuits ZHL-2-8-S+), and then it is coupled with a direct current using a 4.2 GHz bias tee (Mini-Circuits ZFBT-4R2 GW-FT+) to drive a green silicon substrate LED. Before the LED, we use a  
Figure 4: BER versus SNR with different shaping schemes in simu-VLC channel.  
Figure 5: BER versus simulated Vpp with different shaping schemes in simu-VLC channel.  
lens to transmit parallel light to emit it into the water. The green light passes through a 1.2 m tank filled with water whose temperature is controlled at 25 degrees Celsius.  
At the receiver end, another lens is used to focus light on a PIN (Hamamatsu S10784), which converts the optical signal to electrical signals. An iris diaphragm is placed between the lens and PIN to avoid receiver saturation. The electrical signal is then amplified by a pair of differential EAs and is sampled with a digital real-time oscilloscope (OSC, Agilent MSO9254A, Santa Rosa, CA, USA) at a sampling frequency of 2 GSa/s. The received digital signal is equalized by a wave-wise Volterra equalizer in the time domain and demodulated via CAP demodulation. Later, the signal is down-sampled and de-mapped after symbol-wise equalization. At last, we calculate the bit error ratio (BER) and compare the transmission performance among the AE-8 QAM, Norm-8 QAM, and PO-8 QAM. The wavelength of the employed LED is 520 nm, and the \(-10\)dB bandwidth of the LED is 30 MHz. The bias current we use in the experiment is 258 mA and the power is 100 mW according to our measurements.
## 5 Experiments
### First Experiment: Evaluate the Model for STS Tasks
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **STS12** & **STS13** & **STS14** & **STS15** & **STS16** & **STS-B** & **SICK-R** & **Avg.** \\ \hline \multicolumn{8}{c}{**Unsupervised models**} \\ \hline Glove embeddings (avg.) \({}^{\ddagger}\) & 55.14 & 70.66 & 59.73 & 68.25 & 63.66 & 58.02 & 53.76 & 61.32 \\ fastText embeddings \({}^{\ddagger}\) & 58.85 & 58.83 & 63.42 & 69.05 & 68.24 & 68.26 & 72.98 & 59.76 \\ \hline BERT\({}_{\text{base}}\) (first-last avg.) & 39.70 & 59.38 & 49.67 & 66.03 & 66.19 & 53.87 & 62.06 & 56.70 \\ BERT\({}_{\text{base}}\)-flow-NLI & 58.40 & 67.10 & 60.85 & 75.16 & 71.22 & 68.66 & 64.47 & 66.55 \\ BERT\({}_{\text{base}}\)-whitening-NLI & 57.83 & 66.90 & 60.90 & 75.08 & 71.31 & 68.24 & 63.73 & 66.28 \\ IS-BERT\({}_{\text{base}}\)\(\blacklozenge\) & 56.77 & 69.24 & 61.21 & 75.23 & 70.16 & 69.21 & 64.25 & 66.58 \\ CT-BERT\({}_{\text{base}}\)\(\blacklozenge\) & 61.63 & 76.80 & 68.47 & 77.50 & 76.48 & 74.31 & 69.19 & 72.05 \\ SG-BERT\({}_{\text{base}}\)\(\blacklozenge\) & 66.84 & 80.13 & 71.23 & 81.56 & 77.17 & 77.23 & 68.16 & 74.62 \\ Mirror-BERT\({}_{\text{base}}\)\(\blacklozenge\) & 69.10 & 81.10 & 73.00 & 81.90 & 75.70 & 78.00 & 69.10 & 75.40 \\ SimCSE\({}_{\text{unsup}}\)-BERT\({}_{\text{base}}\) & 68.40 & 82.41 & 80.91 & 78.56 & 78.56 & 76.85 & 72.23 & 76.25 \\ TSDAE-BERT\({}_{\text{base}}\)\(\blacklozenge\) & 55.02 & 67.40 & 62.40 & 74.30 & 73.00 & 66.00 & 62.30 & 65.80 \\ ConSERT-BERT\({}_{\text{base}}\)\(\blacklozenge\) & 70.53 & 79.96 & 74.85 & 81.45 & 76.72 & 78.82 & 77.53 & 77.12 \\ ConSERT-BERT\({}_{\text{large}}\)\(\blacklozenge\) & 73.26 & 82.37 & 77.73 & 83.84 & 78.75 & 81.54 & 78.64 & 79.44 \\ DiffCSE-BERT\({}_{\text{base}}\)\(\blacksquare\) & 72.28 & 84.43 & 76.47 & 83.90 & 80.54 & 80.59 & 71.23 & 78.49 \\ miCSE-BERT\({}_{\text{base}}\)\(\square\) & 71.77 & 83.09 & 75.46 & 83.13 & 80.22 & 79.70 & 73.62 & 78.13 \\ \hline RoBERT\({}_{\text{base}}\) (first-last avg.) & 40.88 & 58.74 & 49.07 & 65.63 & 61.48 & 58.55 & 61.63 & 56.57 \\ CLEAR-RoBERT\({}_{\text{base}}\)\(\blacklozenge\) & 49.00 & 48.90 & 57.40 & 63.60 & 65.60 & 72.50 & 75.60 & 61.08 \\ DeCLUTR-RoBERT\({}_{\text{base}}\)\({}^{\ddagger}\) & 52.41 & 75.19 & 65.52 & 77.12 & 78.63 & 72.41 & 68.62 & 69.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Assessment results on the seven STS tasks. [PERSON]’s rank correlation as \(\rho\times 100\) is given for each task in the columns. The best average result is in bold in the last column. \(\ddagger\): [33], \(\ddagger\): [9], \(\vartriangle\): [6], \(\vartriangle\): [10], \(\vartriangle\): [56], \(\blacklozenge\): [4], \(\vartriangle\): [7], \(\lozenge\): [8], \(\blacksquare\): [13], \(\square\): [14], \(\ast\): [15], SBERT-base-nli-v2: reproduced by ourselves, and the rest of the results are taken from Ref. [12].
# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 3 Materials and Methods
### PMVS Dense Reconstruction  
The PMVS (patch-based multi-view stereo) algorithm extracts feature points from multi-view images as initial seed points, represents these points using patches, and performs consistency verification and optimization in multiple views, gradually expanding and densely reconstructing the 3D point cloud in the image. This algorithm generates dense 3D point clouds via image feature matching, viewpoint consistency checking, local optimization, and iterative extension.  
First, one of the images is selected as the reference image. Sparse points reconstructed via SFM are selected as seed points, which can be projected onto the reference image and other images. Then, based on the internal and external parameters of the camera, the pixel depth and normal vector of the sparse point mapping are obtained; the distance and direction between the 3D point and the camera coordinate system origin of the reference image are taken as the initial pixel depth and the initial normal vector of the projection point, respectively. Thirdly, small square patches are created, centered around the selected point. The patch points are mapped onto pixels in other view images. The pixel depth and initial normal vector are optimized by minimizing the reprojection error. Fourthly, the normalized cross-correlation (NCC) photometric consistency method is used to remove noise points. Luminous consistency refers to color consistency within the same point from different perspectives. A small block is projected to be centered on the selected pixel onto a neighboring image, and the similarity of multi-view pixels is calculated. Therefore, the proposed 3D reconstruction method is robust in color and does not rely on color variations. Fifth, the depth and normal value of the seed point are assigned to the adjacent four pixels as the initial depth and normal value of the seed point, respectively. The pixels of other images are found via an epipolar search. The next loop continues until all pixels are searched. Finally, the depth map is reconstructed based on pixel depth and normal vector. The camera's inverse projection matrix is used to project depth pixels into three-dimensional space, and a three-dimensional point cloud is obtained based on the depth of each pixel in the depth map. Finally, Poisson reconstruction is used to generate a three-dimensional model with visualization.
## 1 Measurement and Analysis of Local Humidity
### Measurement of Local Humidity  
Temperature and humidity sensors (DW485N-A) were used. Errors in temperature measurements were less than 0.5 \({}^{\circ}\)C and errors in the relative humidity measurement were less than 4% relative humidity (RH; at 25 \({}^{\circ}\)C). Sensors were placed in the aircraft bays and the data was recorded every minute and transmitted to a computer via wires. To ensure measurement accuracy, a silicon sealing ring was applied to each wiring hole to prevent water from entering the bay and ensure it remained closed. In order to compare the variation patterns of temperature and humidity, humidity measurements in this study were conducted within the same 12-month period as the temperature measurements previously reported [23].  
A total of 15 local temperature measurement points were assessed. Fourteen were located inside the aircraft bay and one was located in the thermometer shelter outside the aircraft. The internal measurement locations are shown in Figure 1.  
The aircraft bays were classified into three types according to how closed or open they are: Open, semi-open, and closed [22]. Measurement points 1, 6, 7, and 9 were located in the open bays, measurement points 4, 11, and 14 were in the semi-open bays, and measurement points 2, 3, 5, 8, 10, 12, and 13 were in the closed bays. Lastly, measurement point 15 was located outside the aircraft.