esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 5 Progress of CO2 Storage in China
### Development Status of CO2 Geological Storage in China
#### 5.1.3 Co\({}_{2}\) Storage in Um-Mineable Coal Seams in China  
When a coal mine reaches the late stage of mining and the coalbed methane content is too low to be easily produced, the mine may be considered un-mineable. Although such coal mines are no longer suitable for coalbed methane mining for economic reasons, the capability of coal seams to absorb CO\({}_{2}\) enables this type of un-mineable coal seams to function as appropriate sites for CO\({}_{2}\) storage.  
China is relatively rich in coal resources. Currently, there are few un-mineable coal mines, so China has not yet carried out any CO\({}_{2}\) storage projects in un-mineable coal seams. However, some studies on technologies and methods of CO\({}_{2}\) storage in um-mineable coal seams and coal mine goafs have been conducted, and the research patent has been released [178], which introduces the key technologies and precautions of the implementation process in detail, laying the foundation for future CO\({}_{2}\) storage in un-mineable coal seams in China.
## 4 Discussion  
Comparing the transmission line constraint in Equation (16) of the proposed risk-averse SUC with Equation (13) as in the conventional UC, we can see that a portion of the total transmission capacity, defined as a new concept of predictive transmission reserves, is maintained by considering the risk of wind power uncertainty-induced congestion. A highly desirable feature of this inequality is that it derives from the chance-constraint in Equation (15) but has a linear form. Therefore, the chanced-constraint SUC framework is recasted as a mixed integer programming (MIP) problem which  
Figure 2: Two-dimensional collocation points (CPs) based on Gaussian quadrature (GQ) rule at level 3: (**a**) Smolyak sparse grids. The total number of CPs is 75; (**b**) Tensor product grids. The total number of CPs is 529.  
can be solved by most commercially available solvers instead of using heuristic methods. In [36], a pre-computed transmission reliability margin is assigned to each line to accommodate various uncertainties using the basic statistical and network information. However, the impact of real-time redispatch on the line flows is not considered, whereas the quantification of transmission reserves in our work is adaptive to the current operating conditions.  
To the best of our knowledge, this is the first time in the literature where the SUC with a predictive model for transmission reserves is formulated, and the reserve requirements are quantified in such a precise manner. While several limitations still exist in the current work that worth further investigating. Firstly, in addition to the wind power uncertainty, other uncertainty sources including the credible \(N-1\) outages of lines and components should also be considered which directly influence the mean and standard deviation of the line flows, as estimated in the real-time redispatch. Secondly, it is worth developing a more comprehensive probabilistic wind power forecast model that accounts for both spatial and temporal correlations between the wind power scenarios. Finally, the Smolyak spares grid used for scenario reduction could be coupled with parallel computing technique [23] to cope with more complex power systems of higher dimensionality.
# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
## 3 Proposed Algorithm for Static UE Scenario
### The Proposed New GRNN-Based Algorithm
#### 3.1.2 Data Processing  
After the preprocessing stage, the data are processed through the model layers, namely an input layer, an output layer, and in between two processing layers, namely a radial basis (pattern) layer, and a regression (summation) layer, as illustrated in Figure 2.  
* The input layer, which has the same number of neurons as the number of inputs, receives channel instances of the learning samples of both the direct and indirect channel path coefficients after preprocessing, as described in the previous section.  
* The pattern layer comprises radbas (Gaussian) neurons, with a total number of neurons equal to the number of training samples received by the input layer. Layer units will store the non-linear relationship constructed between the generated response and corresponding input, e.g., bypassing the input via each unit in the pattern layer. Weights of this layer are computed as the transpose of the input channel coefficient vectors, \(\boldsymbol{P}\), i.e., \((\boldsymbol{P})^{T}\). At the same time, biases are \((0.8326/spread)\), where the spread represents the width of a radial basis activation function used, i.e., spread \(\leq 1\). Then, the \(i\)-th pattern neuron's equivalent transfer function is given by \[P_{i}=\exp(-D_{i}^{2}/2(spread)^{2}\ ),i=1,2,\ldots,m,\] (12) where \(D_{i}^{2}=(\boldsymbol{I}-\boldsymbol{I}_{i})^{T}\ (\boldsymbol{I}- \boldsymbol{I}_{i})\) denotes the squared calculated distance between the input vector \(\boldsymbol{I}\) and the learned vector \(\boldsymbol{I}_{i}\) of the \(i\)-th neuron, and \(m\) is the cardinality of pattern layer neurons.
* The regression layer consists of linear neurons whose weights are set to the target values, denoted by \(T\). Two types of summation operations are performed in this layer, which are symbolized as \(S_{d}\) and \(S_{k}\), defined as \[S_{d}=\sum_{i=1}^{m}\ P_{i}\,\] (13) \[S_{k}=\sum_{i=1}^{m}\ R_{ij}\ P_{i}\,\] (14) where \(R_{ij}\) is the training response of the corresponding \(i\)-th input and \(j\)-th output.
* In the end, the output layer uses a purlin linear activation function to formulate the final optimized output. For each output neuron, the predicted output in the model is calculated as \[v_{k}= \frac{S_{k}}{S_{d}}=\frac{\sum_{i=1}^{m}\ R_{ij}\exp(-\frac{( \boldsymbol{I}-\boldsymbol{I}_{i})^{T}\ (\boldsymbol{I}-\boldsymbol{I}_{i})}{2( Spread)^{2}})}{\sum_{i=1}^{m}\ \exp(-\frac{( \boldsymbol{I}-\boldsymbol{I}_{i})^{T}\ (\boldsymbol{I}-\boldsymbol{I}_{i})}{2( Spread)^{2}})},k=1,2, \ldots,n.\] (15) where \(n\) is the cardinality of output layer neurons; these outputs represent our optimization variables in \((P1)\), with the first neurons representing active beamforming and others representing passive beamforming phases, as will be described below.  
Figure 2: The designed GRNN structure, where \(\boldsymbol{\theta}_{l}=diag(\boldsymbol{\Theta}_{l})\) denotes \(N\) phase shifts of the \(l\)-th IRS.
# Change of Lip Curvature Through Extraction and Non-Extraction Orthodontic Treatment
## 3 Results  
The initial values of SAL depth and angle, and PBL depth were similar between the two groups. Only PBL angle exhibited a slight difference, being greater in the extraction group, 130.6\({}^{\circ}\) vs. non-extraction group,124.5\({}^{\circ}\), although not significantly different (Table 1). Consequently, the comparison of the two groups offered valuable insights into lip morphology change. The paired \(t\)-test results showed significant differences before and after treatment in the U1 tip, U1 cervical, L1 tip, L1 cervical, SAL depth, SAL angle, UL-to-E, and LL-to-E values (Table 2) in the extraction patients. Significant differences in SAL depth and SAL angle were observed before and after treatment only in the extraction patients. However, there were no statistically significant discrepancies in PBL depth and PBL angle pre- and post-treatment in either group (Tables 2 and 3). An independent \(t\)-test revealed significant differences in the U1 tip, U1 cervical, L1 tip, and L1 cervical values, whereas no significant differences emerged concerning soft tissue measurements and interlabial gap (Table 4). In the extraction group, the correlation analysis revealed a significant correlation between SAL depth, SAL angle, PBL depth, and PBL angle change. Interlabial gap differences were associated with SAL angle, PBL depth, and PBL angle. PBL depth was also significantly correlated with UL-to-E and LL-to-E (Appendix A, Table A1). In terms of the SAL angle change (\(\Delta\)SAL angle), multiple regression analysis in the extraction group led to the formulation of the following equation:  
\[\Delta\text{SAL angle}=0.66\ \Delta\text{U1 tip}-0.98\ \Delta\text{L1 tip}-1.58\]  
Based on the position of the mesiobuccal cusp tip, the maxillary first molars in the non-extraction group moved posteriorly by an average of 2.19 \(\pm\) 1.20 mm, while the mandibular first molars moved posteriorly by approximately 2.29 \(\pm\) 2.014 mm (Appendix A, Table A2). Figure 2: The axes of coordinates were indicated as follows: SN line, sella-nasion line; SN7\({}^{\circ}\) (Blue), constructed FH line (Orange); Y-axis N-perp, nasion-perpendicular line (Dotted) (**A**) Linear measurements. SAL depth, distance from A’ to Sn-Ls; PBL depth, distance from B’ to Li-Pg. Right side of the line is positive. (**B**) Angular measurements. SAL angle, angle between Sn-A’ and A’-Ls; PBL angle, angle between Li-B’ and B’-Pg’.
# Silver Sintering for Silicon Carbide Die Attach: Process Optimization and Structural Modeling
## 3 Results
### Reliability Assessment for Pressure Sintering Optimization
This finding is aligned with a literature experimental characterization [18] of a silver sintering layer as function of material density. The proposed model does not calculate the ultimate tensile strength which is expected to decrease with higher void fraction [18]. This fact is not a limitation considering that presented simulation curves are applied for reliability simulations in which strain and stress are much lower than ultimate values. In fact, the performed experimental-numerical characterization would serve to define the input in terms of silver sintering material properties for modeling activities aiming to perform a reliability forecast. Figure 11: Simulated stress–strain curves at 25 (continuous lines) and 200 \({}^{\circ}\)C (dashed lines) for silver bulk and for two different Ag sintering layer porosity (8 and 20%). Figure 10: Stress–strain characteristic at different temperature. \begin{table}
\begin{tabular}{l l} \hline \hline
**Temperature (\({}^{\circ}\)C)** & **Young Modulus (GPa)** \\ \hline \(-\)65 & 186 \(\pm\) 6 \\
25 & 60 \(\pm\) 4 \\
100 & 32 \(\pm\) 3 \\
150 & 21 \(\pm\) 3 \\
200 & 11 \(\pm\) 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental average \(\pm\) standard deviation of the Young modulus at different temperature. Five samples are considered for each temperature level.
## 3 Approach and Architecture
### Applications  
We implemented customized methods to interact with the ARCOMEM crawler and ARCOMEM storage, for example to satisfy the use cases of Section 2. The Crawler Cockpit, as shown in Figure 2, allows archivist to specify or modify crawl specifications, monitor the crawl and do the quality assurance. The Crawler Cockpit is also used to create the final Web archives. Based on a relevance analysis, a semi-automatic method proposes to the archivist relevant Web pages from the ARCOMEM storage that should be preserved. The archivist always has the possibility to include or exclude pages from this selection. Finally, the selected content will be transferred to the WARC files for preservation.  
The Search and Retrieval Application (SARA) (see Figure 3) allows users to search the archives by domain, time and keywords. Furthermore, browsing the archives via different facets, like topics, events and entities, and visualizing the sentiments of Social Web postings complement the end user application. However, the applications are not limited to the described examples. The ARCOMEM system is open to any kind of application that wants to use it. More details about the SARA application are presented in [8].  
Figure 3: Search and Retrieval Application (SARA): result page.  
Figure 2: Crawler Cockpit: crawl overview page.
# Smart Intravenous Infusion Dosing System
## 2 State of the Art
### Automated Systems for Regulating and Closing the Flow of IV Infusion  
This subsection presents the papers dealing with various fluid level detection systems in IV bottle/bag; however, the focus is on the systems for regulating or closing the infusion flow by using a shut-off valve, directional control valve, or pump.  
A system for detecting and regulating the flow of an IV infusion using an optical sensor and an electrically operated valve is presented in [15]. The sensor detects drops through the drip chamber, while each drop activates a timer. After the droplet detection stops or the time between the two-drop detection expires, the timer activates the solenoid valve, which closes the fluid flow. A vision system for detecting and measuring the flow of IV infusion, based on a color background in the form of a matrix, behind a drip chamber, and a digital/webcam to monitor the movement of the droplet, in front, is shown in [16]. Upon the detection of a droplet in the matrix, the corresponding alarm is activated. The IV infusion flow detection and control systems are presented in [17]. The sensor detects the weight of the IV bag and sends signals to the Arduino controller. When the bag is emptied, the controller closes the valve and the return of blood to the infusion is disabled. A method of controlling IV infusion flow by using a sensor module to detect liquid droplets through the drip chamber is shown in [18]. On the basis of the number of drops, the microcontroller calculates the liquid flow and the remaining time for emptying the IV bag, while the actuator regulates the liquid flow, if necessary. A clinical study confirming the efficacy and safety of the use of IV infusion flow monitoring and control devices is presented in [19].  
The advantages of the described systems are continuous detection of IV bottle/bag fluid level [15; 16; 17; 18; 19], alarm in the patient room or at the nurse room [17; 18; 19], flow control valve for infusion fluid [16], pump control [18], shut-off valve [15; 17], patient monitoring system [18; 19], and being tested in hospitals [17; 18; 19].  
The disadvantages of the described systems are that there is no backflow solution [18; 19], that there is no alarm in the patient room or at the nurse room [15; 16; 17; 18], the data are only in the controller [15; 16; 17; 18; 19], there is no monitoring application [15; 16; 17; 19], and that it was not tested in hospitals [15; 16].
## 4 Results and Analyses
### Baseline Model Performance
\begin{table}
\begin{tabular}{l l c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**CGN**} & \multicolumn{3}{c}{**[PERSON]: Read**} & \multicolumn{3}{c}{**[PERSON]: HMI**} & \multicolumn{3}{c}{**[PERSON]: Avg**} \\ \hline
**Training** & **Augmentation** & **Normalization** & **Rd** & **CTS** & **DC** & **DT** & **NnT** & **DC** & **DT** & **NnT** & **Read** & **HMI** \\ \hline \multirow{4}{*}{**CEN**} & (a) None & None & 9.6 & 23.9 & 42.9 & 22.1 & 54.0 & 50.2 & 40.1 & 59.9 & 39.7 & 50.1 \\ \cline{2-11}  & (b) SP & None & 7.0 * & 22.0 * & 36.7 * & 20.5 * & 55.6 & 43.8 * & 35.4 * & 60.3 * & 37.6 & 46.5 \\  & (c) SP + SpecAug & None & **7.0 * & **20.2 * & **36.1 * & **18.8 * & **51.1 * & **40.1 * & **27.8 * & **52.6 * & **35.3 & **40.2** \\ \cline{2-11}  & (d) None & \(\text{VTLN}_{\text{CGN}}\) & 9.3 & 23.6 & 38.8 * & 21.2 * & 53.4 & 45.9 * & 34.9 * & 59.0 & 37.8 & 46.6 \\  & (e) None & \(\text{VTLN}_{\text{Jas-DCDT}}\) & 9.3 & 24.1 & 36.3 * & 21.8 & 54.1 & 42.0 * & 35.5 * & 58.6 * & 37.4 & 45.4 \\  & (f) None & \(\text{VTLN}_{\text{Jas-DCDT}}\) & 9.5 & 24.2 & **35.0 * & 21.2 \(\ddagger\)** & 53.0 \(\ddagger\)** & 41.1 * & 32.8 * & 57.5 * & 36.4 & 43.8 \\ \hline \hline \end{tabular} \(\dagger\)_p_ \(<\) 0.05, \(\ddagger\)_p_ \(<\) 0.01, and *_p_ \(<\) 0.001.  
\end{table} 
Table 6: Results in %WER, with significance levels, for the Dutch ASR when trained on CGN adults’ speech and tested on CGN adult and the native and non-native children’s and teenager’s speech from Jasmin-CGN, split for read speech and conversational speech. The lowest WERs for each speaker group across all systems are highlighted in bold. an average CER of 33.5% on the children's speech test sets, which is better than the best reported results so far of 38.5% averaged over Set C1 and Set C2 [52], indicating that we have a strong baseline. Interestingly, for the read children's speech, the results are highly similar to those for the adult read speech; however, the performance drops dramatically for the conversational children's speech to a CER of 50.8%. A possible reason for the degraded performance of Set C2 is two-fold: first, the difference between read and conversational speech where conversational speech is always harder to recognize than read speech; second, Set C2 contains speech from younger children than those in Set C1.
## 3 Results and Discussion
### Stakeholder's Influence in Implementing CE Principles
#### 3.4.4 Manufacturer  
The product manufacturer was regarded as the fourth most influential stakeholder in implementing CE principles, with a total of 18 points. This ranking highlights the moderate influence manufacturers hold in promoting CE, as they are responsible for producing and supplying the materials and components used in construction. Respondents believe that manufacturers play a crucial role in shaping the material supply chain, and their ability to produce products that align with CE principles is essential for wider adoption. CN1 commented, _\"If manufacturers do not invest in creating circular products, it becomes challenging for the rest of the supply chain to implement CE practices\"_. Furthermore, C2 emphasized the important role of product manufacturers, stating, _\"If manufacturers can supply circular products at affordable prices, it would remove a key obstacle for project owners. It becomes much easier for owners and other stakeholders to justify the investment in circular solutions\"_. Although manufacturers are seen as significant contributors to the implementation of CE principles, A2 believes that their influence is somewhat limited. Their ability to drive the adoption of CE practices depends on market demand for circular products, which is largely set by higher-ranking stakeholders, such as owners and designers.
# 5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
## 3 Current Telecommunications Landscape in Brazil
### Regulatory Environment  
Open RAN was deployed through standardized open network interfaces defined by the Third-Generation Partnership Project (3 GPP), o-RAN Alliance, IEEE, and other standards development organizations (SDOs) and industries.  
The 3 GPP defines Open RAN architecture in 3 GPP Release-15, describing the study on New Radio access technology. Release-16 adds wireless relaying with integrated access and backhaul (IAB) and satellite access support, while Release-17 focuses on the RAN evolution and beyond.  
The Open-RAN Alliance architecture, a global community of MNOs, vendors, and research and academic institutions, is instrumental in shaping the RAN. The focus of its members on introducing intelligence, openness, virtualization, and interoperability is a key factor in the industry's evolution. The group's alignment of software reference development to the Open RAN architecture and specifications is a significant step towards unifying and accelerating RAN deployment and evolution. The o-RAN Software Community, responsible for proposing and testing the integration of solutions, plays a crucial role in this process.  
The Telecom Infra Project (TIP), a partner of the o-RAN Alliance, is a testament to global collaboration to accelerate innovation and solution trades for Open RAN. With more than 500 organizations participating in the TIP, including operators, vendors, integrators and start-ups, the work of the group on three strategic areas: access, transport, core and services; is a collective effort. The creation of requirement documents, white papers, and field test results in the TIP reflects this collaborative spirit.  
In Brazil, Agencia Nacional de Telecomunicacoes (ANATEL) is the regulatory agency for telecommunications. According to Brazilian law, ANATEL certifies that the RAN equipment is high-quality and secure. Products that transmit radio frequencies are considered to be obligatory for homologation.
## 4 Result and Discussion  
### Training and Testing on Original OPPORTUNITY Dataset  
The Deep ConvLSTM model was implemented following the details in the previous literature [33]. In this benchmark model, 113 channels consisting of accelerometers and IMU sensors were used. The distributions of data for the training, validation and test are shown in Figure 4. As shown in Table 3, the validation dataset consists of only one subset of subject 1, which is S1-ADL5. The training dataset consists of samples from subject 1, subject 2 and subject 3. Therefore, the model presents a greater gap between the training and validation loss (Figure 5a).  
Based on the distribution of the dataset as shown in Figure 4, the imbalanced class distribution was clearly observed. The NULL class numbers were high compared to the  
Figure 4: (**a**) Training set distribution, (**b**) validation set distribution, (**c**) test set distribution.  
Figure 5: (**a**) Training and validation loss, (**b**) F1-score on the training dataset.  
other classes in this dataset. The model training result consisting of the loss curve for training and validation are shown in Figure 5. In the model testing phase, the model gave 0.83 in the F1-score and 0.85 in model accuracy. Through the confusion matrix, as shown in Figure 6, it was observed that most classes had low true positive scores, although the F1-score and accuracy of the model were high. This is due to the imbalance distribution of the OPPORTUNITY dataset. Therefore, to solve this problem, further improvements have been made in the algorithm and more experiments need to be conducted.
# Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways
## 3 Methodology  
### Vehicle Detection and Tracking  
The algorithm employed in this paper primarily combines YOLOv5s with DeepSORT. In the context of a closed traffic environment such as a highway, achieving real-time vehicle detection under high frame-rate camera surveillance of rapidly moving vehicles necessitates a detection algorithm that is capable of timely processing and minimizes the possibility of overlooking detections in the image sequence. The original DeepSORT model, based on the Tracking-by-Detection (TBD) strategy, utilizes the Faster R-CNN object detection algorithm. While it performs well in accuracy, its processing speed is comparatively slow, rendering it unsuitable for real-time applications. Consequently, this paper integrates the multi-object tracking algorithm, DeepSORT, with the YOLOv5s object detection algorithm to replace Faster R-CNN, thereby enhancing the multi-vehicle tracking performance of the model.  
The algorithm comprises two main steps. Firstly, it involves computing the cosine distance of feature vectors and the Mahalanobis distance between detection boxes and prediction boxes using the YOLOv5s network. The computed results are then subjected to weighted fusion to generate an association matrix. Subsequently, the Hungarian algorithm is employed to match vehicle detection boxes with tracking boxes. If a successful match is established, output is generated; if not, a reinitialization of the Kalman filter tracker is required.