esa-sceva/satcom-chunk-collection huggingface dataset:
# Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
## 3 Methodology
### Experimental Results
#### 4.2.3 Hyper-Parameter \(\lambda\) Analysis  
In CNIA, \(\lambda\) governs the contribution of the contrastive learning MI InfoMax mechanism (Equation (5)). In this section, we further analyze the multiple values of lambda in [0, 0.5, 1.0, 5.0, 10.0]. The changes in Hits@10 values in WN18 RR-v1, v2, v3, and v4 are depicted in Figure 4. It was observed that inductive relation prediction performance varies with \(\lambda\) values. Both excessively large and small \(\lambda\) values lead to a decrease in model performance.  
When \(\lambda=1\), the results are the worst on the v2 and v3 datasets, and they are moderate on the v1 and v4 datasets, thus indicating that the prediction performance is unstable. Thus, simply setting the weights of the two to be equal is not conducive to maximizing the performance. Notably, the effect is generally better when \(\lambda>1\) compared to \(\lambda<1\), thereby indicating that contrastive learning plays a more significant role than supervised learning.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ablation**} & \multicolumn{4}{c}{**WN18 RR**} \\  & **v1** & **v2** & **v3** & **v4** \\ \hline CNIA & **89.36** & **85.94** & **72.06** & **83.03** \\ CNIA w/o Att & 85.64 & 84.69 & 66.69 & 82.40 \\ CNIA w/o NRF & 86.45 & 85.06 & 71.40 & 72.98 \\ CNIA w/o NRP & 84.16 & 85.10 & 71.81 & 74.07 \\ CNIA w/o CSR & 86.44 & 83.90 & 70.00 & 82.15 \\ CNIA w/o CL & 85.12 & 80.95 & 64.49 & 73.79 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The Hits@10 metric values (%) of the ablation experiment.  
In summary, this discussion establishes that a better performance can be achieved when the coefficient \(\lambda\) is set to five, and it underscored the importance of contrastive learning through the ablation experiments.
## 3 Our Proposed Method
### Dynamic Dataset with Automatic Labeling  
To enhance the network's fitting effect, we first generate a large number of random input symbol combinations for the SCMA system and obtain the received signal set re  
Figure 3: Architecture of our proposed KDCD when \(J=6\) and \(K=4\).  
quired for generating adequate \(K\)-Polygon diagrams for training. It is worth noting that weak signals caused by very low values of \(E_{b}/N_{0}\) (energy per bit to noise power spectral density ratio), as well as overfitting caused by excessively high values of \(E_{b}/N_{0}\), may both negatively impact the BER performance of the decoder. Therefore, in every communication process, the noise \(\mathbf{n}\) should be randomly generated within an appropriate range of \(E_{b}/N_{0}\) values. To determine the optimal \(E_{b}/N_{0}\) values for the received signal set, we test the following scenarios in this paper.  
* S1: We choose some lower values, i.e., \(E_{b}/N_{0}\in[2:2:6]\) dB.
* S2: We choose some higher values, i.e., \(E_{b}/N_{0}\in[18:2:20]\) dB.
* S3: We train the model with a wide range of values, i.e., \(E_{b}/N_{0}\in[0:2:20]\) dB.
* S4: By removing the lower and higher values, we train the model using \(E_{b}/N_{0}\in[8:2:16]\) dB.  
In the PyTorch environment, a significant number of cycles are typically specified to ensure that the entire image training set is traversed during each iteration of training. However, in our scheme, the image batch used in each training cycle is pre-generated on-the-fly by the corresponding component in the received signal set and shares a storage tensor.  
We then develop the idea of automatically tagging \(K\)-Polygon diagrams. The input symbol combination corresponding to the \(K\)-Polygon diagram should be processed as a \(J\)-bit \(M\)-ary number which equals a decimal number. The label for the \(K\)-Polygon diagram should have the same size as the output of the KDCD and be represented as a one-hot tensor [19], where the element indexed by this decimal number is assigned as one individually. Thus, the operation of automatically tagging \(K\)-Polygon diagrams can be expressed as  
\[\begin{array}{l}d=\sum\limits_{j=1}^{J}m_{j}M^{J-j},\\ \mathbf{b}[d]=1,\end{array} \tag{6}\]  
where \(d\) denotes the decimal number, \(m_{j}\) is the input symbol of the \(j\)-th user, and \(\mathbf{b}\) denotes the one-hot label. Since the entire \(K\)-Polygon diagram dataset is not present at any given time and the labeling process is automatic, we refer to the dataset used for training as the dynamic dataset with automatic labeling.  
Dynamic dataset with automatic labeling circumvents the need for manual labeling of offline datasets, streamlining the process of creating complete \(K\)-Polygon diagram datasets and ultimately reducing the time and resource expenses associated with pre-training models.
# Adaptive Modulation Tracking for High-Precision Time-Delay Estimation in Multipath HF Channels
## 2 HF Time-Delay Estimation Signal Model
### Eight-Phase Shift Keying Signal Model  
Within the domain of HF communication, due to the extremely tight spectrum resources, in order to effectively improve the utilization of the spectrum and data transmission efficiency, the 8 PSK (Eight-Phase Shift Keying) modulation method has been widely used. The 8 PSK method utilizes eight different phase states to characterize the 3-bit binary data, and its modulation signal can be expressed as follows:  
\[s(t)=\mathrm{Re}[s_{bb}(t)\cdot e^{i2\pi f_{c}t}] \tag{4}\]  
where \(Re[\cdot]\) denotes the operation of taking the real part, and \(f_{c}\) is the carrier frequency. \(s_{bb}(t)\) is the baseband signal, which can be expressed as follows:  
\[s_{bb}(t)=\sum_{k=0}^{M-1}d_{k}\cdot g(t-kT_{s}) \tag{5}\]  
where \(d_{k}\in\left\{e^{i2\pi m/8}\mid m=0,1,\cdots,7\right\}\) are the 8 PSK modulation symbols, which cover eight different phase states and are used to carry the information; \(g(t)\) is the shaping filter impulse response, which serves to shape the signal to meet the transmission requirements; and \(M\) is the number of codewords in a cycle.  
The transmit signal in the experimental simulation operates at a carrier frequency of 12.12 MHz, with 48 randomly generated code elements as a cycle continuously transmitted using 8 PSK modulation. The signal baud rate is 2400 bps with a sampling rate of 30 MHz. Figure 2a shows the time-domain waveform of the 8 PSK modulated signal, while Figure 2b displays its autocorrelation characteristics. These results clearly demonstrate that the 8 PSK modulated signal exhibits pronounced autocorrelation properties, which establish a robust foundation for subsequent signal processing operations such as time-delay estimation in HF channel environments.  
Combined with the Watterson HF channel model, the received signal model is given:  
\[y(t)=\sum_{i=1}^{T}G_{i}(t)s(t-\tau_{i})+n(t) \tag{6}\]  
where \(T\) is the number of paths for multipath propagation, and \(G_{i}(t)\) is the channel gain function for the ith path. \(n(t)\) is Gaussian white noise; when the Doppler shift and Doppler spread caused by the two magnetic ion components are approximately the same, the channel gain function only needs to be expressed in terms of one magnetic ion component. Then, the channel gain function \(G_{i}(t)\) can be simplified and expressed as follows:  
\[G_{i}(t)=G_{i}a(t)\exp(j2\pi f_{i}t) \tag{7}\]
# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
## 3 Regions of Study and Precipitation Climatology
### East Africa
Historically, the explanation of the EA seasonal cycle with rain seasons migrating from south to north and again south was related to the analogous south-north shift from the equator of the Inter Tropical Convergence Zone (ITCZ) [43]. The ITCZ is described over Africa as the area around the equator, where the dry northeast harmattan and the moist southwest monsoon converge. Although this interpretation has been cited many times in the literature, a recent work demonstrated that during the equatorial rainy seasons there is a lack of co-location between the low-level convergence associated with the ITCZ and the regions experiencing precipitation [44]. Rather, the mean static energy, saturation mean static energy, vertically integrated moisture from the Indian Ocean, topography, large-scale phenomena, and vertical cells over the Indian Ocean were identified as the main factors affecting EA rainfall seasonality [45; 46; 47; 48]. Figure 1: EA region and elevation map (m).
## 1 Introduction
In the primary framework of EEG, the fundamental techniques for capturing signals involve motor imagery (MI) and steady-state visual evoked potential (SSVEP) [8]. These two paradigms offer many methods and potentials. Motor imagery focuses on the psychological foundations of any actions that do not need the activation of muscles [9]. With BCI, a person may see particular motions, like grasping an object, and their brain will convey the command to the controlling equipment that makes these movements happen. Several methods for assisting patients with decreased motor control were provided [10]. Patients are taught to produce more motor brain impulses in the first strategy and to activate tools that improve motor performance in the second. Despite the fact that individuals with acquired motor impairments sometimes have problems with motor connections, the EEG method shows remarkable improvements and continuous changes. The authors of [11] examined 16 chronic stroke patients who utilized a brain-computer interface to obtain input on arm and hand orthotics. Assistive technology helps people with physical limitations engage in a variety of activities, including playing, moving around, and having normal conversations. This device can reduce the stress that carers feel while thinking about individuals with disabilities [11; 12; 13]. Figure 1 shows a generic approach that researchers take when designing a BCI- and motor imagery-based stroke rehabilitation system, and how researchers employ each part of the diagram is illustrated in more detail in the upcoming sections. This comprehensive review provides an overview of BCI-based machine learning and deep learning algorithms coupled with EEG and motor imagery for stroke rehabilitation. It is organized as follows: In Section 2, a brief background about the topic is given. In Section 3, the diverse applications of EEG, machine learning, and deep learning in rehabilitation used by researchers are introduced. How EEG signals are acquired and how motor imagery training for patients is conducted are elaborated on in Section 4. The signal processing and classification approaches are explained in Section 5. Section 6 includes a discussion of the current limitations in research related to rehabilitation using EEG with ML and DL methods. In Section 7, a comparison of EEG headsets for rehabilitation purposes and different datasets used by the researchers included in this comprehensive review are introduced. Section 8 provides the results of EEG and motor imagery-based studies in the last 7 years. Section 9, the last part of the comprehensive review, is a brief conclusion of the contents of the paper. Figure 1 helps in illustrating what will be discussed later in the review. Figure 1: Overview of a brain–computer interface (BCI) system for motor imagery-based stroke rehabilitation.
## 4 Discussion
### The Essential Elements and IoT Architecture Framework to Implement an IAQ Monitoring Study
#### 4.1.4 Other Hardware  
In addition to the decision-making process for sensor purchase, other hardware requirements in the form of networking and connectivity equipment become apparent. It is important to note the various gateways and access points are also required to facilitate the connectivity of the chosen devices to the wider internet and relevant cloud platforms. These are shown in the hardware architecture portion of Figure 5. For all WiFi-based devices, they are connected via industrial grade Telktonika RUT-240 4G access points. These provide WiFi networks that each sensor can independently connect to and backhauls communication to the Internet via a 4G cellular connection. Since LoRaWAN and Monnit ALTA sensors utilize different communication protocols, they further require protocol-specific gateways to backhaul data to and from the internet and cloud services.  
The Monnit ALTA door sensors use a vendor-developed gateway that integrates the proprietary 433 MHz protocol with a 4G cellular module to facilitate communication to the Monnit cloud platform. As LoRaWAN devices communicate with a cloud-based network server, we used a Dragino LPS-8 LoRaWAN gateway connected via wired ethernet to a Teltonika 4G access point to create a link from the LoRaWAN gateway to the internet.
# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 2 Materials and Methods
### Proposed Model  
#### 2.2.1 AM-MCNN-SBiLSTM  
Figure 1 depicts the prediction process of the AM-MCNN-SBiLSTM model. The wind-related dataset is input into the attention mechanism for feature weighting, obtaining the weighted feature sequence. The weighted feature data is then input into the Multi-Convolutional Neural Network (MCNN) for feature extraction. The extracted multi-scale fusion feature information serves as the input for the Stacked BiLSTM (SBiLSTM) networks, capturing the temporal dependencies of the fusion feature sequence. The final forecast value is obtained through a fully connected layer.  
The attention mechanism distinguishes the contribution of different features to the output by learning the associated information in sequence data, facilitating the model's better capturing of critical information. The MCNN employs a parallel processing of convolutional kernels of varying sizes, enabling a more comprehensive and efficient extraction of features across different time scales, thereby enhancing the model's capacity to express features. BiLSTM can capture the long-term trends in feature sequences. By stacking BiLSTM layers, the model's depth is increased, allowing it to learn more complex data patterns and relationships.
## 2 Study Area and Input Data  
### Mississippi River Basin  
In this work, the Mississippi River Basin (MRB) is used as a case study area. Covering an area of about 3,000,000 km\({}^{2}\), the MRB is the largest basin in North America and encompasses about 40% of the area of the United States. Moreover, it is the third largest basin in the world after the Amazon and Congo [17]. It is a region that is densely populated. In 1982, 18 million people relied on the Mississippi for their water supply [18]. This has resulted in intense anthropogenic water use, due to which many large reservoirs have been built up. The Mississippi, with its tributary the Missouri, is the longest (about 3730 km) and largest (about 16,800 m\({}^{3}\)/s) river in North America.  
According to the WaterGAP hydrology model (see Section 2.5), a total of 127 lakes and reservoirs are located in the MRB, from which only nine are defined as lakes and one as a regulated lake. All others are reservoirs. Total water volume variations (defined as the sum of the differences between minimal and maximal volume over the period 2003 to 2016) of 180 km\({}^{3}\) and a surface area of 14,918 km\({}^{2}\) are documented for all water bodies (without rivers). The surface area varies between 3 and 1127 km\({}^{2}\) and the storage changes up to 18 km\({}^{3}\). According to the Global Reservoir and Dam Database (GRanD) [19], a total water volume of about 250 km\({}^{3}\) is stored in the reservoirs of the MRB. The entire study area is shown in Figure 1
## 3 Influence Metrics
### Global Influence  
A typical global centrality metric known as 'Closeness' uses the shortest routes to calculate distances between each node and every other node in the network. However, due to the specific characteristics of DSCNs, this statistic often results in inaccurate estimates. To address these issues, we have developed a unique approach to calculating the shortestpaths that more accurately represents the information propagation patterns within DSCNs over time. With this approach, we subsequently formulated a refined definition of the global influence measure, accounting for these distinctive qualities of DSCNs.  
The frequent partitioning of topologies and intermittent connections that characterize DSCNs often lead to higher storage capacity utilization. The constrained storage space on a device can present a hurdle for efficient routing, particularly if it receives messages more quickly than it can transmit them to the next relay device. This situation can result in uneven load balancing, which significantly impacts the overall routing efficiency within DSCNs. Furthermore, employing an excessive number of devices as relays for a message can introduce unnecessary communication overhead, exacerbating routing performance issues. Hence, achieving a balance between delivery delay, load distribution, and communication overhead becomes imperative when making routing decisions in DSCNs.
# Miniaturized Arrow-Shaped Flexible Filter-Embedded Antenna for Industrial and Medical Applications
###### Abstract  
This paper presents the design and characterization of a coplanar waveguide (CPW) fed, low-profile, and flexible arrow-shaped filtenna for ISM band applications at 2.45 GHz. The antenna design involves an innovative approach incorporating etching slots to achieve miniaturization by 34%, contrasting with a traditional quadrilateral-shaped antenna. After the attainment of desired miniaturization, the unwanted harmonics are also mitigated by deploying simple filtering methodology. A perpendicular rectangular stub is strategically introduced to the feedline, effectively minimizing harmonics across a broad frequency range of 3.3-11.0 GHz. Through simulations and measurements, the results indicate that the antenna's operational band spans from 2.276 to 2.75 GHz, encompassing the entire ISM band (2.4-2.5 GHz). Notably, the antenna demonstrates promising radiation characteristics, including omnidirectional gain of approximately 2.2 dBi and a radiation efficiency exceeding 95%. With a compact overall size of 0.243 \(\times\) 0.200 \(\times\) 0.0005\(\lambda\) (where \(\lambda\) is the free-space wavelength at 2.45 GHz), coupled with wide harmonic rejection property, the proposed arrow-shaped filtenna emerges as a compelling candidate for ISM band applications.  
miniaturized antenna; filtenna; ISM band; flexible electronics; harmonic suppression Article  
[PERSON], [PERSON], [PERSON], [PERSON], S.I. Miniaturized Arrow-Shaped Flexible Filter-Embedded Antenna for Industrial and Medical Applications. _Appl. Sci._**2024**, _14_, 11004. [[https://doi.org/10.3390/appl142311004](https://doi.org/10.3390/appl142311004)]([https://doi.org/10.3390/appl142311004](https://doi.org/10.3390/appl142311004))
# Texture Analysis of Near-Infrared Vein Images During Reactive Hyperemia in Healthy Subjects
## 2 Materials and Methods
### Technologies  
The device emits near-infrared (NIR) light through LEDs, which is more strongly absorbed by hemoglobin in circulating red blood cells than by the surrounding skin. This differential absorption enhances the visual contrast of superficial veins. The reflected radiation is captured by the device and processed by a connected computer, which identifies areas of higher absorption corresponding to veins with higher concentrations of deoxyhemoglobin [29]. A contrast image is then projected on the screen in real time, allowing for clear visualization of the venous pattern.The LEDs were placed approximately 30 cm above the subjects' hands, and images were acquired continuously throughout the entire procedure, generating a video file for each subject at a rate of 60 frames per second.
# Risk Propagation Mechanism and Prediction Model for the Highway Merging Area
### Summary  
Many past studies have focused on the macroscopic factors affecting accident risk propagation and utilized machine learning methods for prediction. However, these studies also faced significant challenges such as poor data quality and difficulty in determining critical parameters at the microscopic level. In this study, we aimed to address some of these limitations by utilizing a simulation approach to investigate risk propagation patterns in highway merging areas. Specifically, our research includes building a detailed simulation dataset and improving the Gaussian plume model for microscopic accident risk prediction in such areas. The contribution of our study is the consideration of both macroscopic and microscopic factors when investigating accident risk propagation patterns. Through our proposed model, the complex mechanism of such phenomenon can be better understood. The detailed simulation dataset and an improved Gaussian plume model were built for the microscopic accident risk prediction in such areas.  
The rest of this paper is organized as follows. Section 2 proposes the concept of accident risk propagation in merging areas and defines the regional risk index. Section 3 establishes a Gaussian plume model of the accident impact, which classifies the accident's impact range. Section 4 studies the accident impact propagation mechanism in merging areas through simulation experiments. Section 5 builds and compares two traffic accident risk prediction models for merging areas. Finally, Section 6 concludes the contribution of this paper and future work for this research.
## 2 Results and Discussion
### Thermo-Gravimetric Analysis of Gelatin and Hydrolyzed Gelatin Peptides  
The TGA showed the loss of weight of the extracted samples as a function of temperature, and it defined their thermal stability; in Figures 6 and 7, the TGA results for G and HGPs are reported. After the first weight loss due to the residual humidity as adsorbed and bound water [39], the trend of weight loss followed the thermal degradation of both G and HGPs starting from 200 \({}^{\circ}\)C. Then, a third step of degradation was probably linked to the degradation of organic salt. From the TGA analyses, it is possible to quantify the quantity of residual ashes and to make a comparison with the incineration performed with the muffle furnace. In the case of TGA, it can be seen that G exhibited a plateau between 850 and 900 \({}^{\circ}\)C, which corresponded to a residual ash mass of 1.8% against the result with a muffle of 3.1%, while HGPs showed the same results in both cases (4.5% and 4.6%) but with the difference that in their cases, the plateau was not visible. It can therefore be deduced that a complete incineration of G requires higher temperatures in the muffle, but not with TGA. On the other hand, HGPs may need a temperature above 900 \({}^{\circ}\)C to achieve the complete incineration in both methods.  
Figure 6: TGA (thermo-gravimetric analysis) of gelatin (G).  
Figure 7: TGA (thermo-gravimetric analysis) of hydrolyzed gelatin peptides (HGPs).  
Determination of Molecular Weight Distribution via Size-Exclusion Chromatography of Gelatin and Hydrolyzed Gelatin Peptides  
The relative size distributions of the peptides in G and HGPs were analyzed using Size-Exclusion Chromatography (SEC). As expected, the analysis showed that the G sample had a higher total average molecular weight compared to the hydrolyzed sample (HGPs), with values of 17,265.5 \(\pm\) 26.2 Da and 2637.5 \(\pm\) 67.2 Da, respectively. Table 4 shows the relative size distributions for the two samples, divided into categories based on size, showing that the majority of peptides in G were of 10,000 Da and above (80.2%). For the HGPs, there was a larger spread in peptide sizes, with the highest relative amount being in the range 1000-2500 Da (41.1%).