esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Contrastive Learning  
Contrastive learning is based on the assumption that representations of similar data should be close to each other in the latent space, while dissimilar data should be as far apart as possible. There are several approaches to contrastive learning, but one that has gained popularity for visual representations is SimCLR (Simple Contrastive Learning of Representations) [17]. The authors of SimCLR simplified certain self-supervised contrastive learning algorithms without the need for custom network architectures.  
During training using contrastive methods like SimCLR [17], a positive pair is sampled from the database, which consists of two representations with the same content, or alternatively, one representation from which a positive pair is generated through appropriate transformations, as shown in Figure 3.  
The input tensor in Figure 3 is denoted as \(x\). Two different augmentation methods, \(t\) and \(t^{\prime}\), selected from the set \(T\), transform the tensor \(x\) into tensors \(x_{i}\) and \(x_{j}\), which are then fed into the encoder \(f(.)\) in the form of a deep convolutional neural network. The output latent representations \(h_{i}\) and \(h_{j}\) from the encoder are passed through the dense layer \(g(.)\), which maps the latent representations to a space where the contrastive loss function is computed. SimCLR [17] defines the contrastive loss function for the positive pair as \"NT-Xent loss\" (Normalized Temperature-Scaled Cross-Entropy Loss) described by Equation (1).  
\[\mathcal{L}_{i,j}=-log\frac{exp(sim(\mathbf{z}_{i},\mathbf{z}_{j})/\tau)}{ \sum_{k=1}^{2N}1_{[k\
eq i]}exp(sim(\mathbf{z}_{i},\mathbf{z}_{k})/\tau)} \tag{1}\]  
Figure 3: Generating a positive pair from a single tensor in contrastive learning.  
The similarity \(sim(\mathbf{z}_{i},\mathbf{z}_{j})\) and \(sim(\mathbf{z}_{i},\mathbf{z}_{k})\) is determined using cosine similarity, as described by Equation (2), where \(\mathbf{z}_{i}\) and \(\mathbf{z}_{j}\) are vectors obtained from the dense layer \(g(.)\) for similar images, and \(\mathbf{z}_{i}\) and \(\mathbf{z}_{k}\) are vectors for dissimilar images.  
\[sim(\mathbf{A},\mathbf{B})=\frac{\sum_{i=1}^{n}A_{i}B_{i}}{\sqrt{\sum_{i=1}^{ n}A_{i}^{2}}\sqrt{\sum_{i=1}^{n}B_{i}^{2}}} \tag{2}\]  
The contrastive loss function includes the indicator function \(\mathbb{1}_{[k\
eq i]}\in\{0,1\}\), evaluating to 1 if and only if \(k\
eq i\), as well as a parameter \(\tau\), which is a tunable temperature parameter used to scale the input to the cross-entropy, directly affecting the feature distance in the latent space.  
During the training of SimCLR [17], a minibatch is sampled from the database, and contrastive prediction is performed on augmented tensors, creating positive pairs. For a specific positive pair, the remaining augmented pairs are treated as negative pairs.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 5 Artificial Intelligence in Solar Plant Monitoring
### Feed-Forward Neural Network  
Feed-forward neural network (FFNN) techniques in the literature have been used for diagnosis in PV systems [37; 61; 62; 63; 64]. The authors in [37] show an accuracy rate of 97.2% by predicting power output drops caused by short open-circuit faults, arrays and string degradation, and shadowing. However, FNNN is reported to have limitations, such as slow training, and risk may be involved in converging to local minimum points instead of the global minimum [37]. To mitigate the limitations in FNNN, Extreme Learning Machines (ELMs) are a suitable alternative and a neural network architecture for fault diagnostics in solar plants reported by [41]. In this work, the ELMs have successfully predicted fault diagnosis for open short circuits and shadowing. The ELMs are trained using historical power generation records and the sensed parameters of the panels, like current, voltage, output power, temperature, and irradiance. Some studies have integrated AI algorithms with additional techniques, such as data fusion and feature engineering for improving fault diagnosis and maintenance in solar plants [65].
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 2 Materials and Method
### Buoy Data  
The wind in-site measurements of the buoys have been collected from the National Data Buoy Center (NDBC) of the National Oceanic and Atmospheric Administration (NOAA), which are used as the ground truth to evaluate the accuracy of the GF3-02 SAR wind speed retrieval results. Due to the limited number of scenes that the collected SAR ocean data covers the NDBC buoy, we chose to match the nearest SAR image within 50 km of the buoy for research. There are a total of 10 matching data located in the waters off  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Orbit ID**} & \multirow{2}{*}{**Beam Code**} & **Center** & \multirow{2}{*}{**Imaging**} & **Num of** & \multirow{2}{*}{**Area**} \\  & & **Incident** & & **Time** & **Used** & \\ \hline  
\hline \end{tabular}
\end{table}
Table 1: GF3-02 SAR sea surface data collected.  
the Hawaiian Islands and the waters off the west coast of the United States, as shown in Figure 2.
## Materials
### GPP Datasets
#### 2.2.2 MuSyQ  
The Multisource Data Synergized Quantitative (MuSyQ) algorithm employs methods similar to the MODIS GPP algorithm, but it differentiates itself by using multiple remote sensing products and implementing certain improvements in the calculation of the LUE by taking into account the finding that the LUE for diffuse solar radiation is higher than for direct solar radiation [56]. Particularly in cloudy regions, and in tropical, evergreen broadleaf forests, the accuracy of GPP predictions can be enhanced by incorporating the clearness index (CI) into the LUE estimation. The MuSyQ dataset was provided by [57]. The flowchart of the MuSyQ algorithm is provided in Figure S2.
## 3 Maintenance and Lifespan Management
### Developing the Means that Enable Signals to be Digitally Acquired
#### 3.1.2 Standardization and Normalization  
One solution for interoperability is to use IEDs under generic object-oriented substation event (GOOSE) messages, as presented by [PERSON] et al. [174].  
[PERSON] et al. [175] present the results of communications between automated grids, based on IEC 61850, and the conclusions are that travel times (messages) are less than 40 ms.  
It is possible to use a complete and integral model based on IEC 61850, as shown by [PERSON] et al. [176]. In addition, the monitoring and control of substations can be achieved through IEC 61970 and IEC 61850, as reported by [PERSON] et al. [177].  
[PERSON] et al. [178] present an approach for unifying data types of IEC 61850 and IEC 61970-301 where authors identify matching, non-matching types and data types.
## 3 Distributed Target Tracking Systems  
In this section, we present the proposed distributed cooperative target tracking system: _Two-level Clustering Approach via Timer_ (TCAT) in a cluster-based network topology, which organizes the tracking task in four phases: tasking leader selection, choosing the sub-cluster members, target positioning, and sub-cluster member reselection and leader handoff. Therefore, the proposed tracking approach organizes a sub-cluster for the tracking task, allows each sub-cluster member to locally compute the target position, and uses cooperation to obtain the fused estimate in the leader node. The local level estimate of a sub-cluster member and the global level estimate of a leader can be derived by a Bayesian and neural networking framework, and the covariance intersection algorithm, respectively. The main assumptions are: (i) all sensors are homogeneous; (ii) the sensors are in fixed and known location; (iii) a pre-specified sub-cluster size \(n\) is applied to perform cooperative target positioning with angle-of-arrival (AOA) information or hybrid time-of-arrival/angle-of-arrival (TOA/AOA) information, (iv) the target periodically broadcasts a message for measurement purpose. Note that these assumptions may be applied to healthcare scenarios or habitat monitoring to locate patients or animals. The distributed tracking architecture in a cluster-based network is depicted in Figure 1.
# Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network
## 3 Modeling And Problem Formulation
### Energy Consumption Model
#### 3.3.1 Data Transmission Energy Consumption  
The data transmission energy consumption consists of two parts: the task-offloading energy consumption, and the migration energy consumption. These are both, essentially, the energy consumed by data transmission, so their formula definitions are the same. Based on FORM, the task transmission energy consumption \(e_{tq}^{i,j,t}\) and the migration energy consumption \(e_{cm}^{j,j,t}\) can be expressed as follows:  
\[e_{to}^{i,j,t}=e_{Tx}(k,d)+e_{Rx}(k)\quad i\in C,j\in F \tag{6}\]  
\[e_{cm}^{j,j,t}=e_{Tx}(k,d)+e_{Rx}(k)\quad j\in F,j\in F \tag{7}\]  
\[e_{Tx}(k,d)=e_{dec}\times k+\epsilon_{amp}\times k\times d^{n} \tag{8}\]  
\[e_{Rx}(k)=e_{dec}\times k \tag{9}\]  
where \(e_{Tx}(k,d)\) is the energy consumed to increase data capacity \(k\) to an edge server at distance \(d\); \(e_{Rx}(k)\) represents the energy that is required to receive \(k\) bits of data. The environment strongly influences the value of the propagation fading index \(n\). If there is no building obstruction, the value of \(n\) can be set to 2 when transmitting data between user devices and edge servers. Otherwise, it can be 3, 4, or 5, depending on the specific environment. \(e_{dec}\) and \(\epsilon_{amp}\) represent the energy consumption constant of the transmission and receiver electronics and the energy consumption constant of the transmit amplifier, respectively.
# Experimental Demonstration of Single-Channel EEG Signal Using 32 x 32 Pixel OLED Screen and Camera
## 3 VL-OCC System Architecture and Applications
### Brief Description of the VLC System  
Recently, the VLC has been preferred for communication because LEDs used as the transmitter in VLC systems have the dual functionality of illumination and data communication. Figure 1 depicts the simple block diagram comprising of three modules namely: transmitter, optical channel, and receiver.  
The transmitter is responsible for transmitting the data information and is the light emitting diode (LED) in the VLC system. The receiver is a photodiode (PD) and the channel characteristics depends upon the application. To maintain the intensity level of illumination, a LED driver is essential. The data transmitted through the LED goes to the optical channel and is then received by the photodiode and then amplified using Transimpedance amplifier (TIA). The use of a camera as the receiver instead of photodiodes in VLC systems reduces the implementation cost considerably [43; 44]. In visible light optical camera communications, modulation schemes such as under-sampled phase-shift on-off keying (UPSOOK) and under-sampled frequency-shift on-off keying (UPSOOK) were used for the communication between the LED and the image sensor of the camera. However, in this paper the authors deployed an on-off keying non-return-to-zero (OOK_NRZ) modulation scheme due to simplicity and ease in representing bits [45; 46]. In order to overcome the challenge of the data rate of the camera, several modulation schemes and image processing techniques have been developed; however, the data rate of a camera is still limited to kbps due to camera frame rate. Hence, depending upon the requirements of data rate and the cost factor, photodiodes and cameras are interchangeably used in visible light communication systems [47; 48]. Similarly, LEDs are extensively used as transmitters; however, they are being replaced by OLEDs due to their low implementation cost, flexibility, and  
Figure 1: Basic functional block diagram of the Visible Light Communication (VLC) system comprising of Light Emitting Diode (LED), Optical Channel, Photodiode (PD), Transimpedance Amplifier (TIA) and Digital to analogue converter (DAC)  
low-power consumption. In this research work, low data rate was the requirement for the transmission of single-channel EEG in LOS condition. The authors were able to achieve the data rate of 2.8 kbps for EEG using VLC and a camera in comparison with the maximum data rate of 2.4 kbps reported in literature, the proposed scheme performed better with data rate being improved by 16%.
# Determination of Hydrogen's Thermophysical Properties Using a Statistical Thermodynamic Method
## 2 Theoretical Model and Implementation  
### Statistical Thermodynamic Model for Hydrogen  
Statistical thermodynamics is a basic tool used to obtain thermophysical properties theoretically. Equilibrium H\({}_{2}\) is composed of specific proportions of ortho-H\({}_{2}\) and para-H\({}_{2}\) at different temperatures. Hence, a statistical thermodynamic method is used to obtain the ortho-H\({}_{2}\) fraction in equilibrium H\({}_{2}\), the para-ortho H\({}_{2}\) conversion heat and thermophysical properties of para-H\({}_{2}\) and ortho-H\({}_{2}\). In this study, subscripts o, p and n in physical quantities represent ortho-H\({}_{2}\), para-H\({}_{2}\) and normal H\({}_{2}\), respectively.
###### Abstract  
It is common to detect a gap between the apical portion of the post and the most coronal part of gutta-percha in a root canal during post and core treatment in endodontically treated teeth. It may compromise the seal that gutta-percha material provides in the root canal filling, leading to apical leakage, which is often thought to be one of the most common causes for failure of endodontic treatment. The prevention of apical leakage to achieve a complete apical seal and its effect on peripapical health of endodontically treated teeth of post and core in long term clinical research remains unanswered. Post and core treatment were provided to 100 patients by undergraduate students in the year 2016-2017. A total of 70 patients in all were called back to the Prosthodonic Clinic for follow-up care after 4 years to assess the clinical result in terms of the gap between the post-operative and residual gutta-percha. The cemented posts were divided into three groups depending on the space between the cemented post end and the remaining gutta-percha: group I revealed no gaps, group II revealed gaps between >0.1 and 1 mm, and group III revealed gaps more than 1 mm. The peripapical index (PAI) was used to determine the apical condition of each tooth. Chi-square test was used for evaluating the data, and a \(p\)-value of 0.05 was considered statistically significant. A total of 28.6% post and core treated teeth were found diseased while 71.4% teeth were found normal. In group I, 90% of the teeth and in group II, 66.7% of teeth were evaluated as normal; however, in group III, 41.2% teeth were found diseased. In endodontically treated teeth restored with a post and core, a gap between the apical end of the post and the coronal section of the residual gutta-percha was associated with a greater prevalence of disease. The post and core treatment by undergraduate students achieved a clinically acceptable outcome in terms of the gap between the post end and the remaining gutta-percha.  
post and core; post gap; post space; prefabricated post; cast post
## 4 Resilience  
The previous sections showed the occupancy and the functionality trends for the selected European countries by considering national data (sources: national websites). Functionality curves were interpolated with polynomial curves to define analytical relationships between the time frame and the functionality. Recovery functions \(Q(t)\) were calculated for each country:  
\[Q(t)=\text{A}\times T^{3}+\text{B}\times T^{2}+\text{C}\times\text{T} \tag{3}\]  
where A, B, C are the coefficients of the interpolating curves (see Table 1). R2 was used to assess the quality of correlations. The selected European countries were compared in order to assess the different strategies and management procedures.  
The results of the analyses in terms of resilience are shown in the last row of Table 1. It is worth noting that the values did not differ significantly, with a more rapid recovery for Italy (1.05) and France (1.01). This could be attributed to several factors, such as the warm climate conditions of the Mediterranean basin in the summer.  
The policies that each national government management adopted were of great importance, confirming what was presented extensively by [3]. The authors showed the role of multi-level politics and public policy. In particular, in France, a hierarchical system of crisis management was quickly established. In Germany, centralization was achieved by building coordination between the federal and regional governments. Italy was affected by weak centralization over the first few weeks (especially for the health sector) and an absence of coordination mechanisms that caused a first phase of severe impacts. The United Kingdom presented a mixed picture, where a system of cooperation seemed to prevail and facilitated the creation of a hierarchical system; more details in [3].  
However, other variables needed to be considered (i.e., economic indicators, frequency and length of lockdowns) to realistically assess the values of resilience. In addition, considering that every country had a different history, approach and management of the crisis was also fundamental. In particular, the results showed that the various national strategies made all the difference in our assessment of resilience. Therefore, an inter-national plan of emergency would have been particularly useful to coordinate the various strategies and unify the European response to global pandemic crises such as COVID-19.  
In this regard, [12] demonstrated the importance of a multidimensional approach based on international cooperation and mutual relationships between authorities and sectors. In addition, [7] recommended some actions to improve resilience (i.e., transfer of patients from hospitals, collaborations among national R&Ds). It is worth noting that having more resilient health systems could allow nations to avoid huge economic consequences. In addition, fragmentation in nations made Europe particularly vulnerable, and this new methodology could help to improve more comprehensive approaches among countries, enhancing their preparedness, surveillance and recovery.
## 7 Topology Control
### Link Constraint Models
#### 7.2.3 Disk Model  
This model requires sensor nodes to be within a specific radius \(r_{n}\) to establish a communication link between them. For the node distribution, it is considered that all n nodes are uniformly and individually deployed in a bounded space of a Euclidean plane. Such network area A is either a torus T or a square S, each of unit area, depending on whether the boundary effect exists or not [98]. The boundary effect arises whenever part of the transmission area of a node may fall outside the network area A. T does not have the boundary effect, whereas S has the boundary effect [99; 100; 101].
## 5 Discussion  
The different configurations of CST feature sizes (original, 1/4, 1/2, 3/4, and full) highlight how scaling affects model performance. Smaller sizes generally provide a compact representation, leading to higher precision but potentially lower recall. Conversely, larger sizes capture more details, improving recall but possibly introducing more false positives. The \"Magnified 1/4 Size\" configuration often achieves a good balance, making it a preferred choice for general purposes. The variability in performance across different instruments suggests that certain instruments benefit more from specific feature configurations. For instance, instruments like drums and organs might require more sophisticated feature combinations or additional attention mechanisms to enhance detection accuracy. Incorporating attention mechanisms at various stages helps the model focus on the most relevant parts of the spectrogram, improving overall performance. The results indicate that these mechanisms are crucial, particularly for instruments with overlapping or subtle features. Further research could explore optimizing CST feature sizes and experimenting with other attention mechanisms. Additionally, applying the hierarchical residual attention network to other audio classification tasks could test its generalizability and effectiveness beyond musical instrument recognition.
# The Application of Fine Sand in Subgrades: A Review
## 3 Treatment Technology for Sand-Filled Subgrades  
### Mechanical Analysis of Cement Treatment Sand  
The treatment methods for sand materials mainly include cement treatment and fiber treatment [15; 16; 17; 18; 19]. In recent years, many researchers have made significant contributions to the understanding of cement treatment behavior [20; 21; 22; 23; 24]. [PERSON] and [PERSON]'s research [25] proposes an innovative approach to improve the strength of desert sand for pavement design by adding manufactured sand, ordinary Portland cement, and fly ash as a binder, demonstrating optimal results with a specific mix ratio of CFA, NDS, OPC, and FA in terms of strength, compaction, and bearing capacity.  
[PERSON] [26] conducted a comprehensive investigation into the strength and durability of cement-treated sand, with a particular emphasis on its response to stress. The study involved experimenting with various mixtures of cement, sand, and limestone powder, which were shaped into different forms to examine the material's mechanical behavior under compression and tension.  
Previous studies by [PERSON] [27], [PERSON] [28], and [PERSON] [29] have also delved into the investigation on cement-treated sands. It is widely acknowledged that the type of cement used is an important parameter in examining the stiffness of cement-treated soils. Additionally, it was observed that the rate at which tangent stiffness increases at the cementitious yield point varies depending on the amount of cement utilized. With an increase in cement content, the bonding and ultimate yield surfaces move upward. The tangent stiffness of cement-treated soil is consistently exceeding that of untreated soil, and this difference is more pronounced at lower confining stresses. However, it diminishes with increasing confining pressure. This trend is observed under both drained and undrained conditions, where the difference between drained and undrained tangent stiffness decreaseswith increasing confining pressure, while the tangent stiffness increases with increasing cement content.  
The study examined strength characteristics were studied by artificially adding Portland cement to sandy soil. The mechanical behavior of the soil was analyzed through unconfined compression tests and drained triaxial compression tests, employing local strain measurements and scanning electron microscopy (SEM) testing. A series of SEM images of the samples indicated that specimens without cement addition or untreated specimens exhibited a more open and porous matrix, whereas those with cement addition displayed a more closed and dense structure.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 3 Methodology  
The TSA-GRU fusion model is composed of two modules for making effective time-series predictions on MOOC data (Figure 1):  
TSA Module: Using a sparse multi-head attention mechanism, this module extracts the most salient temporal features by focusing on certain parts of the sequence.  
GRU Sequential Encoder and Fusion Module: This extracts local sequential dynamics from attention-enhanced features and fuses these global and local representations for the final prediction.  
TSA-GRU adds a new TSA component, where only some time steps are assigned a meaningful attention weight based on a learnable sparsity threshold. TSA-GRU employs a dynamically learned method for filtering temporal noise and irrelevant information. Specifically, this will be interpreted as an improved attention to the most pedagogically relevant moments, contributing to generalization and prediction.