esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Primary Results through Remote Sensing  
The sites' spatial data provided the peak values of every month for a period of two and a half years (June 2018-April 2021). These primary spatial data, as shown in Figure 2, are key to further research and will serve as a basis for multiple conclusions. The data showed the average concentration of each pollutant during the selected time. These results also highlight the location responsible for the highest contributor of each pollutant in the air globe of Lahore.  
The average CO concentration of all sites in the data set was 41.1 millimol/m\({}^{2}\). The maximum value was for Jail Road (58.982 millimol/m\({}^{2}\)), and the minimum was at Jia Bagga (34.57 millimol/m\({}^{2}\)). NO\({}_{2}\) average accumulation was 0.1010 millimol/m\({}^{2}\), with the maximum value of 0.255 millimol/m\({}^{2}\) obtained from Jail Road, and the minimum value of 0.037 millimol/m\({}^{2}\) from Kot Abdul. The SO\({}_{2}\) concentration on average in Lahore was 0.428 millimol/m\({}^{2}\). The maximum value obtained was 0.747 millimol/m\({}^{2}\) at Jia Bagga, and the minimum was 0.232 millimol/m\({}^{2}\) at Karol. The CH\({}_{4}\) average value was 1.914 millimol/m\({}^{2}\), the maximum was 1.993 millimol/m\({}^{2}\) in Quaid-e-Azam Industrial Estate, and the minimum was 1.860 millimol/m\({}^{2}\) in Sundrad Industrial Estate. The HCHO on average was 0.236 millimol/m\({}^{2}\). The maximum value was obtained from Main Boulevard at 0.376 millimol/m\({}^{2}\), and the minimum from Jail Road at 0.079 millimol/m\({}^{2}\). The average AOD value in Lahore was 0.54 (550 nm) or 0.98 \(\mu\)m. The maximum value was 2.27 \(\mu\)m from Jia Bagga, and the minimum was 0.46 \(\mu\)m. According to the U.S. EPA standard, the safe range for AOD is 0.1 \(\mu\)m-0.4 \(\mu\)m [36]. The ambient air of Lahore did not meet this standard, not for a single time, at any location. The maximum value was 5.6 times higher than the upper limit of the safe range. This calls for deep interpretation of this data for practical and immediate adaptation action plans.
## 2 Related Works
### GCN-Based and Other Related Deep Learning Methods
Its rapid detection makes YOLO suitable for real-time applications, such as disaster monitoring and urban planning, where it can quickly identify land cover changes. For instance, [PERSON] et al. [46] applied YOLO in flood monitoring to swiftly pinpoint affected areas. However, while YOLO excels in object detection, it may lack precision in complex segmentation tasks requiring high detail, prompting researchers to enhance it by integrating other deep learning techniques for better accuracy. Long short-term memory (LSTM) [47] networks are a type of recurrent neural network (RNN) designed for handling long-term dependencies in sequential data. Originally developed for language models and time series, LSTMs have proven effective in remote sensing image segmentation due to their ability to capture time- or space-varying patterns. They are particularly useful for processing multi-temporal remote sensing data, allowing models to learn relationships between images from different time points and detect land cover changes. For example, [PERSON] et al. [48] applied LSTM to monitor urban development and crop growth, accurately identifying land cover changes. However, LSTMs require substantial training data and computational resources, which can limit their practical application, especially for high-resolution images. Nevertheless, advancements in technology are enhancing the feasibility of LSTM models in remote sensing image segmentation, particularly for tasks needing high spatiotemporal resolution.
# Auditory Localization in Low-Bitrate Compressed Ambisonic Scenes
## 4 Results
Based on the obtained results we can infer that the localization precision depends slightly on the bitrate (within the examined bitrate values). Figure 8: Median localization error at different codec bitrates/Ambisonic orders. The whiskers indicate non-parametric 95% confidence intervals [47].
# Underlying Factors and Strategies for Organizational BIM Capabilities: The Case of Iran
## 4 Analysis and Results
### PLS-SEM Analysis  
Next, Structural Equation Modeling (SEM) was used to test the proposed hypotheses. Covariance-based SEM (CB-SEM) and partial least squares SEM (PLS-SEM) are the two forms of SEM. PLS-SEM was chosen over CB-SEM because it is better able to handle non-normal datasets and small sample sizes [104]. It is also best used for exploratory research with theoretical models that are not well-developed [105]. PLS-SEM produces two sets of models: a measurement model and a structural model. First, the validity of the measurement model is assessed using composite reliability, loadings of indicators, and Average Variance Extracted (AVE). To assess the internal consistency reliability of the measurement model, the value of composite reliability should be more than 0.70 [106]. Indicator loadings are used to assess indicator reliability, which should be greater than 0.70 [106]. Then, the convergent validity is assessed using the value of AVE, which should be greater than 0.5 [106]. After that, discriminant validity is assessed using indicators cross-loadings. Finally, the validity of the structural model is evaluated utilizing the importance and relevance of the structural model relationships.
## 5 Conclusions and Future Works  
The versatility of the piezoelectric effect has contributed to new uses of piezoelectricity in modern technologies, for example, energy harvesting. The piezoelectric effect is employed in this paper to convert mechanical energy present in the form of vibrations and movements into electrical energy. A piezoelectric floor is designed to transform the mechanical energy of the footfalls of people into electric energy. The main conclusions are:  
* The performance offered by different types of piezoelectric transducers and different sizes has been studied. The macro fiber composite piezoelectric that generated more voltage at the output in the experiments was the MFC M-0714-P2 type. However, the piezoelectric PZT generated 93% higher output power under the same conditions.
* A study on the distribution of forces along the footfall has been carried out for the arrangement of piezoelectric PZT. The voltage generated individually by each piezoelectric was analyzed to determine those areas where more pressure was exerted to optimize future piezoelectric platforms.
* A study has been carried out of the power generated by the platform for people with different weights and for three different directions of their steps.
* Finally, a basic business study has been conducted according to commercial platforms.  
Conceptualization, C.Q.G.M. and G.Z.A.; methodology, C.Q.G.M. and G.Z.A.; validation F.P.G.M.; formal analysis, C.Q.G.M. and F.P.G.M.; investigation, G.Z.A. and C.Q.G.M.; resources, F.P.G.M. writing--original draft preparation, C.Q.G.M.; writing--review and editing, F.P.G.M. and C.Q.G.M.; funding acquisition, F.P.G.M. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Company—Technology** & **Generated Power** & **Price** & **Prototype Platform** \\ \hline Waynergy Floor & 10 W per step & 400\(\upc\) & Lower price \\ Sustainable Energy Floor (SEF) & 7 W per step & 800\(\upc\) & Lower price \\ Pavegen Tiles & 5 W per step & 1800\(\upc\) & Lower price \\ \hline Sound Power & 0.05 W per steps & Unknown & Better performance for a smaller \\  & 0.02 mW/cm\({}^{2}\) & & surface, (30 cm side vs. 50 cm side) \\ \hline PZT ceramic & 8.4 mW & Unknown & Better performance \\  & & & (35.6 mW for 70 kg) \\ \hline Parquet PVDF layers & 2.1 mW & Unknown & Better performance \\  & & & (35.6 mW for 70 kg) \\ \hline Drum Harvesters, Piezo & 2.46 mW & Unknown & Better performance \\  & & & (35.6 mW for 70 kg) \\ \hline POWER leap PZT & 0.5 mW & Unknown & Better performance \\  & & & (35.6 mW for 70 kg) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of piezoelectric platform with commercial solutions.  
Funding: The work reported herewith has been financially by the Direccion General de Universidades, Investigacion e Innovacion of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102).  
The authors like to thank [PERSON] (Birmingham University, UK) for his detailed revision of the context.  
The authors declare no conflict of interest.
## 4 Analysis
### Other Effects
#### 4.2.2 Buffer Occupancy  
When using jitter in a flooding operation, the flooded packets must be stored in buffers in the intermediate routers for the time imposed by the jitter interval. The average occupancy of intermediate routers' buffers is obviously related to the distribution of jitter values.  
This section computes the average number of packets to be forwarded by a particular router at time \(t=t_{0}\), for each of the different jittering distributions studied. Assuming a Poisson process with rate \(\lambda\) for packet arrival, the number of packets that arrive (on average) within \([t_{0}-J_{m},t_{0})\) is \(\lambda J_{m}\). The probability that one of these is scheduled to be sent later than \(t_{0}\) is \(Pr(\bar{T}_{t}+T_{j}>t_{0})\), where \(\bar{T}_{t}\) stands for the random variable of the arrival time (uniformly distributed within \([t_{0}-J_{m},t_{0})\), according to the Poisson model) and \(T_{j}\) is the random variable for jitter value.  
Then, the average buffer occupancy \(B\) has the following expression:  
\[B = \lambda J_{m}Pr(\bar{T}_{t}+T_{j}>t_{0}) \tag{11}\]  
The statistical distribution of \(\bar{X}=\bar{T}_{t}+T_{j}\) is determined by the jitter value distribution:  
* For the uniform jitter, \(Pr(\bar{X}>t_{0})=\frac{1}{2}\).
* For the window jitter (\(\alpha\in(0,1)\)), \(Pr(\bar{X}>t_{0})=\frac{1+\alpha}{2}\).  
Since the use of window jitter entails, for the same value of \(J_{m}\) and the same flooding rate \(\lambda\), a larger average delay before retransmission than uniform jitter, buffers are expected to store, on average, a \(\alpha\) additional fraction of packets to be forwarded.
## 3 Response Models
### Model Fitting
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{8}{c}{**E-24h Response Models**} & \multicolumn{8}{c}{**Rc,24h Response Models**} \\ \hline  & **Full** & **Fractioned A** & **Fractioned B** & **Small** & **Full** & **Fractioned A** & **Fractioned B** & **Small** \\ \hline \multirow{2}{*}{**Model Terms**} & **F24h** & **F24h** [MPa]** & **F24h** [MPa] & **F24h** [MPa] & **Rc,24h** [MPa] & **Rc,24h** [MPa] & **Rc,24h** [MPa] & **Rc,24h** [MPa] \\  & [MPa] & **F24h** & **F24h** & **Rc,24h** [MPa] & **Rc,24h** [MPa] & **Rc,24h** [MPa] & **Rc,24h** [MPa] \\ \hline Independent & 11.43 & 11.91 & 11.32 & 11.24 & 60.65 & 60.69 & 60.61 & 60.66 \\ Vw/Vc & \(\underline{-}\)**0.4053** & \(\underline{-}\)**0.4101** & \(\underline{-}\)**0.3776** & \(\underline{-}\)**0.4101** & \(\underline{-}\)**6.170** & \(\underline{-}\)**6.180** & \(\underline{-}\)**6.180** & \(\underline{-}\)**6.100** \\ Sp/p & NS & \(-\)0.0106 & NS & NS & NS & NS & NS & \(-\)0.589 \\ \hline \hlineA high correlation between the response and the factors was found in the data presented in Table 12. The D-flow and T-funnel models demonstrated a strong influence of the factor Vs/Vm and Vw/Vc. As expected, the F24h and Rc,24h models confirmed the strong influence of the factor Vw/Vc (the volume of water to the volume of cement) on the strength of the cement-based mixtures. The models also showed the inversely proportional influence of other factors containing sand, powder and water.
# Benchmarking Message Queues
## 2 Background
### ActiveMQ Artemis [4]  
ActiveMQ Artemis is an open-source message broker that can be used to send, receive, and store messages. It provides a messaging system that is designed to be scalable and high-performing and can be deployed in a variety of environments, including standalone applications, microservices, and cloud-based systems.  
ActiveMQ Artemis is built using a modular architecture that allows it to be easily extended and customized to meet specific messaging requirements. It is written in Java and supports multiple messaging protocols, including AMQP (Advanced Message Queueing Protocol) [5], MQTT (Message Queuing Telemetry Transport) [6], STOMP (Simple Text Oriented Messaging Protocol) [7], and OpenWire [8].  
As a message broker, ActiveMQ Artemis acts as an intermediary between producers and consumers of messages. It receives messages from producers and stores them until they can be delivered to the appropriate consumers. This allows applications to communicate with each other in a decoupled manner without the need for direct point-to-point communication.  
ActiveMQ Artemis supports several messaging patterns, including point-to-point and publish-subscribe. Point-to-point messaging involves sending a message to a specific destination, such as a queue, whereas publish-subscribe messaging involves sending a message to multiple destinations, such as topics. ActiveMQ Artemis also provides support for advanced messaging features, including durable subscriptions, message grouping, and message routing. It also supports clustering, which allows multiple brokers to be connected together to form a single, highly available messaging system.
## 2 Related Work
DyGIE++ [7] extended the DyGIE model by integrating global and local information to assist tasks, and uses BERT to construct span representations, while SpanProto [16] introduced global boundary matrices to learn span boundary information and uses prototype learning and margin-based learning to train the model. [PERSON] et al. [17] proposed a span-based joint extraction framework that generates semantic representations for specific spans and contexts based on attention. [PERSON] et al. [18] proposed a new span representation method called the Padding and Layered Marker (PL-Marker), which strategically wraps markers in the encoder to consider the relationships between spans, especially using a neighborhood-based packing strategy to model entity boundary information more effectively. [PERSON] et al. [22] constructed a memory module to remember the category representations learned in entity recognition and relation extraction tasks, and design a multi-level memory flow attention mechanism to enhance bidirectional interaction between entity recognition and relation extraction. These models perform entity and relation classification as two-stage subtasks in a single model. However, enumerating all spans could cause a problem of sample imbalance, as most sentences likely contain few relations between entities. SpERT, a span-based model proposed by [PERSON] et al. [14], addresses this issue by sampling spans before entity classification and generating negative samples that are strongly negatively related to the positive samples, thereby balancing the training data. However, the approach described above overlooks the impact of lexical information on the span-based models. [PERSON] et al. [23] proposed a method that considers the advantages of Bi-LSTM in capturing bidirectional semantic dependencies and the attention mechanism in assigning different weights to different parts-of-speech features of words, and combines them to perform entity-relation extraction. [PERSON] et al. [24] proposed a BERT-BiLSTM-CRF model that integrates part-of-speech (POS) features and regularization methods (BBCPR). However, lexical features and the structures with multi-head self-attention, such as transformer encoder, have not been utilized by span-based models. This may enhance the rich semantic expression of spans based on the advantage of resolving overlapping entities in a span-based model and improve the performance of entity and relationship recognition by filtering effective information through multi-head self-attention. In this paper, we propose a method that enhances the span using multi-level lexical features. Additionally, we introduce an attentional sequential learning structure based on the transformer encoder in the relationship extraction process, which enables the adaptive capturing of intrinsic connections and contextual information between span pairs, improving performance of ERE tasks.
# A Lithology Recognition Network Based on Attention and Feature Brownian Distance Covariance
###### Abstract
[12] developed a rock recognition model based on convolutional neural networks, achieving a recognition accuracy of 63% for 15 common rock sample images. [PERSON] et al. [13] established an intelligent recognition method for rock properties using transfer learning of rock images, recognizing 30 rock types with an accuracy rate of 90.21% and demonstrating robustness and generalization. [PERSON] et al. [14] proposed a multi-scale lithology recognition method based on deep learning of rock images, achieving a recognition accuracy exceeding 85%. Despite these advancements, current deep learning-based rock properties recognition methods face challenges [15; 16; 17], including (1) insufficient data sample sizes affecting feature learning during model training and (2) complexities in the background of tunnel rock face images, including illumination and shadows, hindering accurate recognition of rock properties. This paper addresses the challenges associated with lithology recognition by proposing a Feature Convolutional Attention Residual (FC-Res) network. The network employs ResNet18 as the backbone network, integrating a feature Brownian distance covariance (FBDC) module and an improved Convolutional Block Attention Module (CBAM). The key contributions of this paper are outlined as follows:  
* Design of the feature Brownian distance covariance module: We propose a FBDC module inspired by the concept of depth Brownian motion distance covariance. This module is designed to quantify the disparity between the joint eigenfunctions of embedded features and the edge product, aiming to enhance the attention to sample features. * Improvements to the CBAM: An improved CBAM is proposed, which mitigates the interference of background information by a multilayer perceptron (MLP) in the rising channel attention module. * Application of FC-Res network: The proposed FC-Res network was employed for the identification of rock properties at construction sites, specifically in the Kangding, Laolinggang, Xingdongshan, and Yigong tunnels. This application can greatly solve the problem of poor prediction results due to insufficient training samples at construction sites.
## 2 Literature Review
### Knowledge Distillation for Compression
KD was used to counter the model performance degradation caused by binarization by transferring knowledge from the _teacher_ network to the _student_ network. With the use of KD, the performance loss was reduced to only 1% in the F1 score (0.88 to 0.87). On the other hand, the memory usage of the binarized network was 30.2 times smaller. In [32], the authors used KD to compress an arrhythmia classification model based on 12-lead ECG signals into a single-lead ECG model. The dataset contains labeled 12-lead ECG signals obtained from 10646 individuals. The _teacher_ is a convolutional neural network consisting of 17 deep convolutional layers, which is trained on the 12-lead signals. The _student_ contains five deep convolutional layers and is trained on only one of the 12 leads, along with the knowledge received from the _teacher_. The _student_ model showed significantly lower memory consumption and a higher compression rate with no degradation in model performance. The authors of [33] proposed an ECG classification model for single-lead portable devices created from the compression of a 12-lead model using KD. A generative adversarial network (GAN) is used to match the feature dimensions between the _teacher_'s 12-lead ECG and the _student_'s single-lead ECG. The GAN generates one-dimensional data by treating a single-lead ECG as an input feature and the other 11 lead signals as extended features. Two _teacher_ models were developed to ensure model distillation performance. The dataset contains 3518 labeled single-lead ECG samples from portable devices and 100,000 12-lead ECG samples. The final model showed an improvement of 30% in the F1 score compared to the baseline model trained on only single-lead ECG signals (49.54% to 79.30%). The authors of [45] proposed a distilled ECG classification model for small edge devices using KD. The dataset used was the MIT Arrhythmia Database, which contains ECG records from 48 patients, each 30 min long. The _teacher_ model was a deep neural network with 8 layers consisting of fully connected, max pooling, one-dimensional, and activation layers. The soft labels generated by the _teacher_ were included in the _student_model's training. The _student_ model achieved a lower loss and runtime but with a drop in accuracy. In [46], the authors proposed an ECG classification model for CVD detection using KD. The dataset contained 43191 standard 12-lead ECG recordings with 24 unique labels. The proposed model had a ResNet-based architecture, while the _teacher_ model had an SE-ResNet-based architecture. The authors introduced a Multi-Level Knowledge Distillation (MLKD) approach to reduce the number of leads and retain the knowledge gained from the large model trained on multi-lead ECG data. The MLKD method can be sequential or parallel and involves two teachers: T1 with 12 input channels and T2 with \(n<12\) input channels. The final distilled model had 106 times fewer parameters than the _teacher_ model; moreover, it achieved a 75% decrease in inference time and a 3.2% increase in performance.
## 2 Background and Preliminaries
### The Feature Learning Process of CNN
#### 2.2.1 Convolution Layer  
In the convolutional neural network, the convolution layer, the activation layer and the pooling layer are fixedly combined to form a nonlinear hidden layer. As shown in Figure 5a, the 2-D convolution kernel characterizes the local texture for the image, and the 1-D convolution kernel characterizes the tone for the sound signal. In the case of success, the width of the neural network model, that is, the number of convolution kernels of each convolution layer, is generally set according to the specific conditions of the identification task. The size of the convolution kernel is generally small, and the entire image shares the same weights. This reduces the computational complexity and gives the model translation invariance.  
Figure 4: The basic architecture of the convolutional neural network (CNN) model.  
The convolution process of obtaining the \(j\)-th feature map \(\mathbf{X}_{j}^{C_{l}}\) in the \(l\)-th convolution layer can be expressed as follows:  
\[\mathbf{X}_{j}^{C_{l}}=f\!\left(\sum_{i\in M_{j}^{S_{l-1}}}(\mathbf{X}_{i}^{S_{l-1}}*\mathbf{ W}_{j}^{C_{l}}+b_{j}^{C_{l}})\right), \tag{12}\]  
where \(\mathbf{X}_{i}^{S_{l-1}}\) denotes the \(i\)-th feature map generated from \(l\)-1 th pooling layer \(S_{l-1}\); \(\mathbf{W}_{j}^{C_{l}}\) denotes the weight matrix of the \(j\)-th filter in the \(l\)-th convolution layer; \(b_{j}^{C_{l}}\) denotes the \(j\)-th element of the bias of the \(l\)-th convolution layer; \(M_{j}^{S_{l-1}}\) denotes the subset associated with \(\mathbf{X}_{j}^{C_{l}}\) in the set of feature maps output from the \(l\)-1 th pooling layer and '\(*\)' denotes the 2-D convolution operation. The activation function \(f(\cdot)\) is the core of machine learning, which turns the model into a nonlinear model to enhance the expressive power of the model. In this paper, the rectified linear unit (_ReLU_) is employed, which is defined as follows:  
\[ReLU(x)=\left\{\begin{array}{ll}x&x>0\\ 0&x\leq 0\end{array}\right.. \tag{13}\]  
As shown in Figure 5b, the output gradient is always 1 on the positive half-axis, so the model can converge quickly. However, the negative half-axis output is always zero, which endows the model with good sparsity. Recent research cases have shown that the _ReLU_ can effectively solve the gradient diffusion problem and show better generalization capacity than the saturating nonlinearities such as the sigmoid and tangh functions with respect to large scale training datasets [48].