esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Attacker Profiles  
There are various schemes for radio spectrum sharing and all assume explicitly or implicitly that wireless terminals and base stations (henceforth called \"nodes\") conform to the various rules (being dictated either by protocols, e.g., IEEE 802.22, or by regulators, such as the FCC in the USA, or by other third party entities, e.g., ISP consortia, _etc._). Unfortunately, the above statement (that all nodes act legitimately, abiding by each network's rules and etiquette) is not always true.  
While the system designer specifies protocols with the maximization of a system-wide metric in mind (e.g., high aggregate throughput, fairness and low delay), _rational_ (or _selfish_) nodes seek to maximize their own utility, irrespective of the performance of their peers and the overall community welfare. Nodes, or groups of nodes, then may exhibit various kinds of non-conformant or anomalous behavior. It should be noted that non-compliance with the specified rules is not always a result of node rationality; equipment failures or pure malice, _i.e._ irrational misbehavior not guided towards increasing one's payoff, may account for that. [PERSON] [26] defines selfishness as a rational practice and malice as an irrational one, while [PERSON] \(\mathfrak{a}\) and [PERSON] [27] make the distinction between rational and malicious misbehavior in wireless networks, but propose that they should be jointly tackled.  
In this spirit, we distinguish between _conformant_ and _non-conformant_ entities and, in particular, consider only those that misbehave deliberately and not due to limited functionality or faulty equipment. A first attempt to identify and categorize non-conformant nodes in the Cognitive Radio Networking context is made by [PERSON] _et al._[28]. Their classification is slightly modified and briefly summarized below:  
* _Malicious nodes_ are nodes violating the rules on purpose, without even necessarily attempting to obtain direct (short-term) benefit. Their goal is to cause disturbance either to the underlying network as a whole, or to selected (victim) nodes.
* _Rational nodes_ are those whose aim is to increase their utility (gained by using the underlying network), mainly by using more spectrum resources (bands, or larger time frames, or codes _etc._) than those assigned to them or agreed to by them (possibly implicitly). Rational nodes may attempt to determine unused resources and use them against explicit or implicit allocations, with no negative effect to others, _or cheat, i.e.,_ attempt to maximize their payoff by degrading the performance of others (which are cheated out of their allocated resources).  
Rational (strategic) entities are the most interesting category to consider because they are the ones responding to incentives, which need to be carefully designed to lead the system into good operating practices.
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
## 4 Vitamin D Deficiency and Cardiovascular Risk
### Vitamin D Deficiency and Acute Myocardial Infarction  
Referring back to the previous paragraph, regarding coronary heart disease, the association between Vitamin D and acute myocardial infarction (AMI) deserves further investigation. Indeed, many studies present conflicting results. Some authors demonstrate an increase in Vitamin D deficiency in patients who have suffered an AMI both in Italy and in the United States [39; 52]. Another study carried out on 314 subjects hospitalized for AMI highlights that 85% of patients had 25(OH)D deficiency regardless of sex or age, but it is associated with some risk factors such as diabetes mellitus and calcium deficiency [53]. This trend has also been confirmed in other prospective studies which state a high incidence of Vitamin D deficiency in patients admitted to hospitals for AMI [54; 55]. Further data have been provided on the association between low Vitamin D levels and AMI. [PERSON] et al. in a large study show the link between Vitamin D deficiency and AMI or acute coronary syndrome in 1259 hospitalized patients [56]. As mentioned above there are also conflicting results regarding the association between Vitamin D deficiency and AMI. [PERSON] et al. show how VDR KO mice subjected to experimental AMI had normal cardiac function compared to wild type mice [57]. These results highlight the importance of the contribution of Vitamin D to AMI and other cardiac complications such as thrombosis, arrhythmias, and heart failure, which should still be studied in depth.
## 6 Possible Root Causes and Their Verification  
Knowing that it was the crack lines that caused the observed failure mode, and that these lines were generated because of the high strain developed during the de-penalization process, a strain gauge measurement was performed on the PCB at three test locations, as marked by the black color blocks in Figure 11, near the edge of the PCB during the V-cutting process to ascertain the root cause. The strain gauge measurement results are depicted in Figure 8. One can see the localized areas along the de-paneling line at the bottom side with a high strain rate and high strain around 1400-2050, which was beyond the acceptable criteria according to IPC-WP-011 [24].  
ANSYS WorkBench 19.0 was also employed to examine the strain induced by de-penalization. A 3D CAD model of the PCB was designed in ANSYS WorkBench and a V-cut cutter was designed to mimic the real-world scenario in the simulation software where mechanical strain was observed, as shown in Figure 11. Our simulation results showed the maximum stress area (red area) distributed in strips along the edge of the PCB and this coincided with the burn-out area. The maximum strains did not distribute uniformly, which agreed with our X-ray micrograph and the strain gauge measurement results. [PERSON] [25] also reported that the stress generated through the V-cut process is significantly high in comparison with other cutting processes that cause mechanical cracks in PCBs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Steps**} & **Failure Analysis** & **Details and Findings** \\ \hline
1 & Reception of Failed Device & Customer reported unavailability of ethernet \\ \hline
2 & \
\begin{tabular}{c} Detailed Examination and \\ Failure Mode Inspection \\ \end{tabular} &
\begin{tabular}{c} \(\bullet\) \\ •  
In the pizza cut de-penalizing, the applied mechanical stress caused cracks on the copper keep-out area of the PCB, as shown in Figure 12. However, without de-paneling, there was no crack on the copper keep-out area, as can be seen in Figure 13.  
It was, therefore, clear that it was the pizza cutter de-penalization that caused the cracks in the PCB, which then resulted in moisture diffusion into the PCB. As the cutting area was near the GND plane and V\({}_{\text{IN}}\) plane, as can be seen in Figure 2, the reported failure mode was observed. We found that no damage was observed if a manual de-penalization was performed. Thus, we believed that a proper V-groove design and machine setting could solve this issue, but this was beyond the scope of this work. With proper settings such as the incident angle, rotary speed, and cooling fluid, such damage can be minimized, as in the case of wafer dicing [26].  
Figure 11: PCB de-panel process and mechanical simulation result. Locations 1, 2, and 3 show strain gauge measurement points.  
Figure 12: Observation of cracks in a PCB after de-paneling using an Olympus BX51 microscope at 50 \(\times\) magnification of: (**b**) location No. 1 and (**c**) location No. 2, as marked in (**a**), which is a horizontal cross-section of a PCB with a pizza cutter de-panel.
## 4 Classification of Routing Protocols
### Cluster-Based Routing Protocols
#### 4.3.1 Hybrid Indirect Transmission (HIT)  
In [60] the authors proposed a data gathering protocol named Hybrid Indirect Transmission (HIT) based on hybrid architecture of one or more clusters where each cluster is capable of multiple multi-hop transmissions. HIT uses parallel processing both in intra-cluster and inter-cluster communication to minimize the energy consumption as well as the network delay. The details of the HIT procedure are as follows: initially, one or more cluster heads are elected and then these cluster heads broadcast their status throughout the network to form one or more clusters. After cluster formation, upstream and downstream relationships for each cluster are formed due to which multiple routes are figured out within each cluster from the sensor nodes to the cluster heads. Next each node finds its blocking set which is a list of nodes not allowed to communicate at the same time with that node. Finally, after calculating the Time Division Multiple Access (TDMA) schedule, the sensor nodes transmit the sensed data to the cluster head through upstream neighbors.
## 3 Methodology
### Unsupervised Image Fusion  
Given a set of intermediate denoising images \(\{d_{1},d_{2},\ldots,d_{n-1},d_{n}\}\), the final denoised image \(x_{\text{f}}\) for the pixel positioned at \((i,j)\) is computed as a weighted average of pixels in the same position in denoised instances \(\{d_{1},d_{2},\ldots,d_{n-1},d_{n}\}\)  
\[x_{\text{f}}(i,j)=\sum_{k=1}^{n}w_{k}(i,j)\times d_{k}(i,j), \tag{9}\]  
where the weight map \(w_{k}\) is generated from the proposed unsupervised fusion network, and \(k\) is the index of the denoised instances, \(k=1,2,\ldots,n-1,n\). The family of weights \(\{w_{k}(i,j)\}_{k}\) satisfies conditions \(0\leq w_{k}(i,j)\leq 1\) and \(\sum_{k}w_{k}(i,j)=1\). Figure 7 shows the weighted fusion scheme of the intermediate denoising images. The fusion network has a symmetric encoder-decoder architecture with skip connections. We denote \(\{d_{1},d_{2},\ldots,d_{n-1},d_{n}\}\in\mathbb{R}^{M\times N\times C}\) as \(n\) feature maps from a single image. We first concatenate \(\{d_{1},d_{2},\ldots,d_{n-1},d_{n}\}\) along the channel axis as \(D\in\mathbb{R}^{M\times N\times nC}\), and then \(D\) is taken as the learning target of the fusion network. The output of the fusion network \(W\in\mathbb{R}^{M\times N\times n}\) contains \(n\) channels, and the feature map of each channel \(w_{k}\in\mathbb{R}^{M\times N\times 1}\) is used as the weight map of \(d_{k}\in\mathbb{R}^{M\times N\times 1}\).  
The fusion network is learned in an unsupervised manner, and the loss function measures the sum of the MSEs between the fusion image \(x_{\text{f}}\) and each intermediate denoised instance \(d_{i}\)  
\[L_{\text{total}}^{\text{f}}=\sum_{i=1}^{n}||x_{\text{f}}-d_{i}||_{2}^{2} \tag{10}\]  
Figure 6: Framework of the intermediated denoising image generation.  
In Equation (10), to minimize the loss \(L_{\text{total}}^{\text{f}}\), the sum of the differences between pixels in \(x_{\text{f}}\) and the pixels of the corresponding position in the intermediate denoised images should be the smallest. This means that the fusion image preserves the common details in the intermediate denoised images so that it can achieve a better image quality.
## 3 Energy Dilemma: Coal Resources and Power Demand
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sr. No.** & **Location of Coal Field** & & **Coal Resources (Billion Tons)** \\ \hline \multirow{7}{*}{1} & & \multicolumn{2}{c}{Lakhra} \\  & & Sonda & \\  & & Thatta & \\ \cline{1-1}  & & Jherruck & \\ \cline{1-1}  & & Thar & \\ \cline{1-1}  & & Haji Coal and others & \\ \cline{1-1}  & & Eastern Salt Range & \\ \cline{1-1}
2 & Punjab: & Central Salt Range & 0.235 \\ \cline{1-1}  & & Makerwal & \\ \cline{1-1}  & & Khost-Sharg-Harnai & \\ \cline{1-1}  & & Sor Range/Degari & \\ \cline{1-1}
3 & Balochistan: & Duki, Mach-Kingri & \\ \cline{1-1}  & & Musakhel Abegum & 0.217 \\ \cline{1-1}  & & Pir Ismail Ziarant & \\ \cline{1-1}  & & Chamalong & \\ \cline{1-1}
4 & Khyber Pakhtunkhwa & & 0.092 \\ \cline{1-1}
5 & Azad Jammu Kashmir & & 0.009 \\ \hline \multicolumn{2}{c}{Total} & \multicolumn{2}{c}{\(\sim\)185} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Total coal resources in Pakistan. Figure 4: Key power generation sources in Pakistan. demand for coal to meet the nation's energy requirements. Keeping in view that the demand for coal is too high and is increasing every year, the supply is still significantly low. As discussed earlier, coal is an economical source of energy, and its adequate utilization could replace the expensive sources of energy for different industrial purposes like power generation after the cement brick kiln industries. In Figure 7 (modified after [29]), it can be seen that the top consumers of coal are the cement and brick kiln sectors, with 54.30% and 41.50%, respectively. Next, the remaining amount of coal is consumed by coke and power generation, with 1.70% and 2.50%, respectively. According to the Worldometer estimation [30], the total consumption of national coal is 0.3%; however, 99.7% of proven coal reserves are still untapped due to a lack of modern technology, financial constraints, and lack of awareness regarding its appropriate development. Figure 5: Sector-wise power consumption in Pakistan. Figure 6: Coal production, consumption, and import from 2011 to 2022 in Pakistan. To conclude, Pakistan is facing a severe scarcity of energy due to a lack of indigenous coal consumption and a lack of political consensus on energy issues. It is time for the government, stakeholders, and other related organizations to play their roles in addressing the energy crisis in the country. Moreover, the proper implementation of recommendations may tackle the crisis and play an important role in socioeconomic and sustainable development. The interrelationship between mining safety, modern techniques, and coal resource utilization is crucial for meeting a country's energy demand. By improving safety protocols and adopting advanced mining methods, coal production can be significantly enhanced, thereby closing the gap between coal supply and demand while reducing the need for costly imports. This will profoundly impact the country's energy landscape and contribute to economic growth.
## 3 Methodology
### Acquisition and Adjustment of the Surface Triangular Mesh
#### 3.3.2 Adjustment of the Surface Triangular Mesh  
After acquisition of the surface triangular mesh, it is necessary to check whether the order of all surface triangular mesh nodes is the same. For two triangles with a common edge, if the order of the nodes is counterclockwise, as shown in Figure 6, the order of the nodes of the left triangle is 1-2-4-1. The order of the right triangle is 2-3-4-2, where the ordering of the nodes of the common edge is reversed.  
The adjustment steps of the surface triangular mesh nodes are as follows:  
1. The order of the three nodes of a triangle is adjusted to be counterclockwise, and the triangle is marked as T1, indicating that the order of the nodes of the triangle has been adjusted.
2. The neighboring triangles of T1 that have shared edges are searched. If the order of the shared edge nodes of the neighboring triangle is counterclockwise, the order of the nodes of the triangle is adjusted and the triangle is marked as T2.
3. The neighboring triangles of T2 that have shared edges are searched. If the order of the shared edge nodes of the neighboring triangle is counterclockwise, the order of the nodes of the triangle is adjusted and the triangle is marked.
4. This cycle is repeated until all the triangles are marked.
## 4 Experimental Analysis and Results
### Dataset  
The first multilabel Arabic dataset for target-specific stance identification, namely MAWQIF, is used. It contains 4121 tweets for three different topics in different dialects of Arabic, making up the accessible dataset. Three tasks are offered by MAWQIF: specifically, stance, sentiment, and sarcasm. Hence, MAWQIF can act as a new multilabel benchmark scenario. The dataset is available in ref. [30]. The distribution of the dataset is explored in Table 1, such that the data is divided into two parts: 80 for training and 20 for testing. One example of the dataset instance is given in Table 2, and we visualize the distribution of these classes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Topic of Tweets** & \(\sim\)_1u, \(\sim\)_2u, \(\sim\)_3u, \(\sim\)_4u, \(\sim\)_5u, \(\sim\)_6u, \(\sim\)_7u, \(\sim\)_8u, \(\sim\)_9u, \(\sim\)_19u, \(\sim\)_19u, \(\sim\)_19u, \(\sim\)_20u, \(\sim\)_19u, \(\sim\)_20u, \(  
Based on Figure 2, we see that a large number of Arabic tweets in the dataset have positive and not sarcastic labels about the topic of the tweets. If ML models are used on top of this dataset, they can accurately predict the positive and not sarcasm classes. However, the fewer tweets of the negative and sarcasm classes might be overwhelmed by the majority classes. Therefore, it is sensible to generate or collect more instances for minority classes to have a good representation for making the ML models classify the instances accurately and work well.  
The detail of the multilabel MAWGIF dataset is presented in this subsection. The preprocessing process is performed to convert every text sample to a binary classification problem. Therefore, every sample has only yes or no, meaning zero or one. After that, we apply different types of representation with different classification models. Finally, we discuss the results of the classifiers. Table 2 explores one example with its translation to the English language.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**No.** & **Original Arabic Text** & **Translated To English Text \({}^{*}\)** & **Sentiment** & **Sarcasm** & **Stance** \\ \hline \multirow{7}{*}{1} & \multirow{7}{*}{1} & I also thank the Custodian of the & & & \\  & & Two Holy Mosques and His & & & \\  & & Highness the Crown Prince for & & & \\  & & their concern for the health of the & & & \\  & & citizen, and I also thank the & & & \\  & & Minister of Health for his & & & \\  & & distinguished organization and & & & \\  & good reception. & & & & \\ \hline \end{tabular} \({}^{*}\) This column is a translation of the original text from Arabic to English language.  
\end{table}
Table 2: Examples of multilabel Arabic text by the MAWQIF dataset.  
Figure 2: Distribution of classes in the training and test sets, respectively: (**a,b**) are the distribution of sentiment classes; (**c,d**) are the distribution of sarcasm classes; and (**e,f**) are the distribution of stance classes.
## 4 Mathematical Model  
Using computational fluid dynamics (CFD), researchers can obtain fundamental data on large numbers of samples in a simpler and more economical manner than with experiments. The current numerical simulation methods of multiphase flow include the Eulerian-Lagrangian method and the Eulerian-Eulerian method, the latter of which requires less calculation and is more suitable for the calculation of liquid-solid two-phase flow under high solid holdup.
# New Taipei City Smart Pavement Management Center and Road Maintenance Analysis
## 2 Intelligent Inspection Planning
### System Data Setup  
This study describes a self-managed pavement management platform established by the road construction contractors. This platform divides the contracted maintenance areas into 100 m road segments, with detailed information for each segment, including basic data from inspection activities such as inspection date, weather conditions, and location. The system calculates various pavement condition indicators, including the E-PCI, AARI, PCI, and IRI values, and records them over time. Using this set of contractor-specific design requirements, pavement damage characteristics are quantified and presented numerically for the owner's reference. Below are the data for the road segments and each inspection block.
# Simultaneous Determination of 12 Marker Components in Yeonkyopaedok-san Using HPLC-PDA and LC-MS/MS
###### Abstract  
Yeonkyopaedok-san is a traditional Korean medicine used in the early treatment of boils. In the present study, its 12 marker components for quality control were determined using high-performance liquid chromatography (HPLC) with photodiode array detection and ultra-performance liquid chromatography-mass spectrometry with tandem mass spectrometry (UPLC-MS/MS). The investigated 12 marker components of Yeonkyopaedok-san were as follows: 3-caffeoylquinic acid, cimifugin 7-glucose, liquiritin apioside, ferulic acid, narirutin, 5-\(O\)-methylvisammioside, naringin, neohesperidin, oxypeucedanin hydrate, arctigenin, glycyrrhizic acid, and 6-gingerol. The analytical column used for the separation of the 12 marker analytes in Yeonkyopaedok-san was a Waters SunFire C18 column (4.6 mm \(\times\) 250 mm, 5 um). The two mobile phases used were 0.1% (_v/v_) aqueous formic acid and 0.1% (_v/v_) formic acid in acetonitrile. In the UPLC-MS/MS analysis, all components were separated using a Waters ACQUITY UPLC BEH C18 column (2.1 mm \(\times\) 100 mm, 1.7 um). The two mobile phases used were 0.1% (_v/v_) aqueous formic acid and acetonitrile. The coefficient of determination of the calibration curves in both analysis systems showed good linearity (>0.99). The amounts of the 12 marker components in Yeonkyopaedok-san determined using HPLC-photodiode array detection and UPLC-MS/MS analyses were found to be 0.14-9.00 mg/g and 2.35-853.11 ug/g, respectively.  
HPLC-PDA; LC-MS/MS; simultaneous determination; traditional herbal formula; Yeonkyopaedok-san Article
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 8 Future Directions in 6G Technology Development
### Intensive Academia-Industry Research and Development Works  
Although 5G has not reached its full commercial deployment, some nations have developed particular policies to create 6G in recent years [3]. In Finland, an eight-year initiative with a budget of USD 290 million, dubbed the 6 Genesis Flagship program, was initiated in 2018 to build a 6G ecosystem [131]. Aside from the United Kingdom and Germany, which are investing in 6G technologies such as quantum technology, the United States has begun investigating THz-based 6G mobile networks, while China has stated its intention to create 6G. The development of 6G has also piqued the interest of several businesses, including a large test and measurement vendor, Keysight Technologies, with the potential to dominate the worldwide market in 6G technologies, services, and solutions. Several large corporations, including Ericsson, Huawei, Nokia, and Samsung, have stated their commitment to 6G research and development [132; 133]. Furthermore, Ericsson, Nokia, and SK Telecom have formed a strategic partnership in 6G research [134].  
Another collaboration is between the Massachusetts Institute of Technology (MIT) and Ericsson [135]. MIT and Ericsson aim to conduct two research projects which focus on the pattern of state-of-the-art hardware to power the 5G and 6G mobile networks. The speed, computation power, and energy efficiency of cognitive networks are the main topics of the two research projects. The first project will explore essential studies on the computer materials and chips based on lithium-ion that replicate the human brain's structure in order to consume only a fraction of the energy of today's silicon-based chip designs. The second project focuses on discovering the methods, such as energy harvesting from radio signals and other sources, and undemanding power system designs to accomplish simple tasks, such as autonomous connection and control with minimal to zero need for charging.