esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The ICCs values were all \"excellent\" [17] as they ranged between 0.902 (Co-Go) and 0.992 (Co-Gn) for linear measurements and between 0.954 (ANB) and 0.995 (Co-Go-Me) for angular measurements. Random error ranged between 0.27 mm (OVB) and 1.45 mm (Co-Go) for linear measurements and between 0.32\({}^{\circ}\) (SNB) and 1.48\({}^{\circ}\) (Inf.-P.Mand) for angular measurements. Figure 3: Cephalometric measurements for the analysis of airway dimensions. No statistically significant differences were observed between treated and control samples' demographic data reported in Table 1.  
From the comparison between the treated and the control groups, no statistically significant differences were observed for any of the cephalometric variables at baseline (Table 2), including skeletal, dentoalveolar, and airway dimensions. Descriptive statistics and comparison of changes in the T1-T2 interval between the treated group and the control group are reported in Table 3.  
From the comparison between the MARA group and the control group (Table 3), no statistically significant differences were observed for any of the variables analyzed to evaluate the sagittal dimensions of the airways. On the contrary, considering the variables expressing sagittal skeletal relationships, during active treatment, the MARA group showed a significant improvement of Wits and ANB angle (\(-1.0\) mm vs. \(0.4\) mm and \(-0.7\)deg vs. \(-0.1\)deg, \(p=0.009\) and \(p=0.038\)) compared to the control group. Analyzing the dentoalveolar changes, the MARA group showed a statistically significant reduction in the overjet (\(-2.2\) mm vs. \(-0.3\) mm \(p=0.001\)), of the overbite (\(-1.4\) mm vs. \(0.1\) mm \(p=0.005\)), an improvement of molar relationship (\(1.6\) mm vs. \(-0.2\) mm \(p=0.000\)), and a greater proclination of the lower incisors compared to the control group (\(3.2\)deg vs. \(0.5\)deg \(p=0.012\)).
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 2 Model of Cross-Medium Communication Channel
### Verification of Cross-Medium Communication Channel Model  
A water-to-air cross-medium communication system was constructed to validate the model's accuracy. The equipment and system diagrams are depicted in Figure 3. A single frequency acoustic signal was emitted underwater from a UW:30 speaker. The surface waves, triggered by the acoustic signal propagating to the water surface, were detected using the AWR1642 mm wave radar board provided by TI. The inside of the metal box containing the water and the power cable of the speaker were silenced using silencer cotton, which minimized the impact of external sound, the box, multipath interference, and the vibration of the power cable at the water surface.
## 3 Proposed Sensor Design Method
According to Figure 4, to ensure that the 1 PPS does not fall in the skip area of \(f_{n}\), the lower boundary of \(\phi_{0}\) must be greater than a 10.23 MHz clock period and the upper bound must be less than \(2^{N}\) minus an \(f_{c}\) clock period. We can then obtain \(\phi_{0}\in\left(\frac{2^{N}}{n},2^{N}-\frac{2^{N}f_{n}}{f_{c}}\right)\). After this approach, as per Equation (10), the skip area in Figure 4 becomes \(\left(\phi_{0}-\frac{2^{N}f_{n}}{f_{c}},\phi_{0}\right)\), as shown in Figure 5, where the value of \(\phi_{0}\) is \(2^{N-1}+\frac{2^{N-1}}{n}\). Then, we make \(f_{c}\) track \(f_{n}\) using the PLL. At the sampling time \(t^{\prime}{}_{2}\), it yields  
\[p_{1}=rem\left(\frac{p^{\prime}{}_{0}+\Delta p}{n2^{N}}\right). \tag{11}\]  
Figure 5: Transferring the skip area of the 10.23 MHz by \(f_{n}\). Thus, as per Equation (11), as long as \(n\) is greater than one, \(\phi_{0}\) will be greater than \(\frac{2^{N}-p_{0}^{\prime}-\Delta p}{n}\) and a phase skip problem does not occur. Then, \(p_{compensate}\) can be expressed as:  
\[p_{compensate}=n\cdot(p_{1}-p_{0}). \tag{12}\]  
The entire process flow of sensor is depicted in Figure 6.  
Through phase compensation and the conversion of \(p_{compensate}\) to time, the revised pseudo-range ranging formula is  
\[ \begin{array}{l}T_{B}{=}\Delta t+t_{A}+\tau(t_{1},t_{2})+r_{B}\\ =t_{2}-t_{0}+\frac{p_{compensate}}{2^{N}10.236}\\ =t_{2}-t_{0}+\frac{n(p_{1}-p_{0})}{2^{N}10.236},\end{array}  \tag{13}\]  
where \(N\) represents the bit width of the numerically controlled oscillator (NCO) of the 10.23 MHz phase.
## 2 Materials and Methods
### Context Diagram  
Figure 2 presents the context diagram. In this schematic representation, the boundaries of the system and the interactions and relationships between the system's actors are presented visually. The system is presented by a circle AgriGame at the center of the diagram. External entities (technology providers, research institutes, developers, etc.) are shown schematically as squares and are involved in developing such serious agricultural games. Data flows are depicted as arrows showcasing the information flows between external entities and the depicted system [54].  
\begin{table}
\begin{tabular}{c c} \hline
**Question** & **Ranking** \\ \hline Interest in Collaborative Projects for Innovation & Rate your interest in engaging in collaborative projects with research institutions for vineyard innovation. 1 is not interested, 4 is potentially interested, 7 is very interested. Rate from 1 to 7. \\ \hline \multirow{4}{*}{Marketing Strategies for Wine and Vineyard} & Rate your primary method for marketing your wine and vineyard. 1 is online sales and marketing, 4 is a balanced mix of channels including direct sales and distribution, and 7 is direct sales at the vineyard or local markets. Rate from 1 to 7. \\ \hline \multirow{4}{*}{Future Perspective on Viticulture and Winemaking} & Rate your perspective on the future of viticulture and winemaking in your region. 1 is pessimistic, facing significant challenges, 4 is cautiously optimistic, acknowledging some challenges, 7 is optimistic, foreseeing growth and innovation. Rate from 1 to 7. \\ \cline{1-1} \cline{2-3}  & Rate your perspective on the future of viticulture and winemaking in your region. 1 is pessimistic, facing significant challenges, 4 is cautiously optimistic, acknowledging some challenges, 7 is optimistic, foreseeing growth and innovation. Rate from 1 to 7. \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._The overall structure of this context diagram follows conventional software architecture approaches (e.g., UML or standard systems modeling languages), as documented in prior works on game-based or educational systems. What is novel here is our specific emphasis on representing real-time interactions with physical agricultural environments and data sources, such as sensor arrays, and on delineating the roles of AI-powered virtual educators.
# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 3 Results  
This research focuses on identifying factors that may affect the availability of the information required by data centers for early warning diffusion on seismic and volcanic phenomena. Therefore, this section reviews relevant studies related to four fields: (a) data transmission networks, (b) data acquisition and processing, (c) seismic data standards, and (d) early warning trends. However, it is essential to mention other, yet no less important, processes that involve the communication system of these organizations. These are data storage, analysis, and diffusion of information for subsequent research in geophysics, geology, seismology, and volcanology. Each of these fields was compared through an individual analysis, obtaining summaries described in Tables 1-4, with methods, characteristics, application areas, and approaches. Figure 5 shows the distribution by year of the publications that are reviewed in this study and distributed by application areas.  
From selected articles, most of these date from 2015 to the present. The trend shows that between 2016 and 2021 seismological organizations have emphasized improving seismic data transmission by including quality parameters and an evaluation of seismic networks such as [26; 31; 36]. In the same way, for the areas of data acquisition and processing, as well as early warning trends, there are many studies which demonstrate a rising trend over the last 4 years, such as [43; 46; 49]. The application and acquisition of information systems have been associated with the determination of early warnings to help with the detection of earthquakes and seismic location, the evaluation of magnitudes, and use of seismic data with other SDC, among others. On the other hand, the formatting trend for seismic data processing is more constant, because seismic data standards have reached maturity and global diffusion. Therefore, they are used by most seismological  
Figure 5: Year of publication per applications areas.  
networks worldwide. The main objective is the exchange of data between these global networks. Several of the selected studies [18; 55; 58] show that seismological communities have implemented protocols and software solutions to unify data acquisition systems and their compatibility in the last decade. However, data processing methods have been unified to a lesser extent; these solutions are ad hoc in most cases.
# A New Knowledge-Distillation-Based Method for Detecting Conveyor Belt Defects
## 3 Result and Discussion
### Experimental Results of Knowledge Distillation Algorithm  
To verify the effectiveness of the knowledge distillation strategy used in this paper, YOLOv5m was used as the teacher network, and YOLOv5n and YOLOv5n-slim were used as the student network with mini-networks. In this paper, the network after YOLOv5n distillation was named YOLOv5n(KD), and the network after YOLOv5n-slim distillation was named YOLOv5n-slim(KD). The test results of the model are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Pruning Rate/\%** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]/\%** & **Params/M** & **Inference Time/ms** \\ \hline YOLOv5n & 0 & 86.02 & 93.84 & 95.57 & 1.76 & 1.7 \\ YOLOv5n-slim-0.2 & 20 & 87.70 & 88.50 & 92.92 & 1.49 & 1.5 \\ YOLOv5n-slim-0.3 & 30 & 89.28 & 84.91 & 92.75 & 1.22 & 1.4 \\ YOLOv5n-slim-0.4 & 40 & 89.14 & 88.90 & 92.64 & 1.01 & 1.3 \\ YOLOv5n-slim-0.5 & 50 & 85.43 & 86.65 & 89.58 & 0.86 & 1.3 \\ YOLOv5n-slim-0.6 & 60 & 83.17 & 87.40 & 89.10 & 0.68 & 1.2 \\ YOLOv5n-slim-0.7 & 70 & 82.22 & 88.70 & 90.74 & 0.49 & 1.2 \\ YOLOv5n-slim-0.8 & 80 & 81.72 & 85.51 & 86.68 & 0.32 & 1.1 \\ YOLOv5n-slim-0.9 & 90 & 83.70 & 78.21 & 83.50 & 0.17 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of model pruning.  
Figure 10: Distribution of BN layer scaling factors (**a**) Original training; (**b**) Sparse training.  
To compare the performance of the models after distillation, three models of different sizes of YOLOv5 were tested in this paper, as can be seen from Table 3: (1) In the YOLOv5 series of models, the corresponding detection accuracy and inference time increased as the model increased from YOLOv5n to YOLOv5m. (2) The pruned network YOLOv5n-slim decreased by 4.83% in [EMAIL_ADDRESS], 72.16% in the number of parameters, and 29.41% in the inference time compared with that before pruning. (3) After the introduction of knowledge distillation, the detection accuracy of the model was relatively significantly improved compared to the previous one, where YOLOv5n(KD) improved by 1.76% compared to [EMAIL_ADDRESS] before distillation, and [EMAIL_ADDRESS] decreased by 1.11%, parameter volume decreased by 91.56% and inference time decreased by 83.17%, compared to the teacher network YOLOv5m. Compared with the network YOLOv5s, which is larger than it, a 75.66% reduction in parameter volume and a 63.83% reduction in inference time were achieved at the cost of a 0.2% reduction in [EMAIL_ADDRESS]. (4) The pruned student network YOLOv5n-slim showed a 4.09% improvement in [EMAIL_ADDRESS] after distillation, compared with the teacher network YOLOv5m [EMAIL_ADDRESS] which decreased by 2.92%. Additionally, the amount of network parameters decreased by 97.65%, and the inference time decreased by 88.12%. This shows that the knowledge distillation algorithm used in this paper can effectively improve the accuracy of the detection model without increasing the complexity of the model.
# Assessment of the Resource Potential of Baltic Sea Macroalgae
## 3 Results
### Seasonal Dynamics of Beach-Cast Macroalgae
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{4}{c}{**Content, \%**} \\ \cline{2-5}  & _Ulva_ & _Cladophora_ & _Polysiphonia_ & _Furcellaria_ \\ \hline Glucose & 4.51 \(\pm\) 0.13 & 1.63 \(\pm\) 0.04 & 2.31 \(\pm\) 0.07 & 1.73 \(\pm\) 0.04 \\ Xylose & 1.10 \(\pm\) 0.03 & 0.69 \(\pm\) 0.01 & 0.34 \(\pm\) 0.01 & 0.44 \(\pm\) 0.01 \\ Fucose & 2.32 \(\pm\) 0.07 & 2.71 \(\pm\) 0.10 & 4.13 \(\pm\) 0.10 & 6.49 \(\pm\) 0.10 \\ Arabinose & 1.24 \(\pm\) 0.06 & 3.38 \(\pm\) 0.08 & 0.64 \(\pm\) 0.01 & 0.43 \(\pm\) 0.02 \\ Rhamose & 0.39 \(\pm\) 0.01 & 1.22 \(\pm\) 0.03 & 0.37 \(\pm\) 0.01 & 0.32 \(\pm\) 0.01 \\ Galactose & 1.66 \(\pm\) 0.04 & 1.85 \(\pm\) 0.05 & 1.18 \(\pm\) 0.03 & 2.51 \(\pm\) 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Carbohydrate composition of macroalgae (% of the content of reducing carbohydrates). The predominant components of the fatty acid composition in the biomass of algae samples from the genera _Polysiphonia_ and _Ulva_ were tribehenate glycerate (content \(0.70\pm 0.08\%\) and 0.8 \(\pm\) 0.06%, respectively) and methyl palmit (0.40 \(\pm\) 0.08% and 0.60 \(\pm\) 0.07, respectively), and the content of behenic acid was at the level of 0.2%. The predominant components of _Furcellaria_ biomass were methyl palmitate and tribehenate glycerate (content 1.2%), and methyl oleate and methyl stearate (about 0.2%). In _Cladophora_ algae, a high content of methyl palmit (0.80 \(\pm\) 0.09%) and tribehenate glycerate (content at the level of 0.6%) was noted. The content of C, H, N, S, and O is a significant biomass parameter that affects the quality of the resulting biofuel, which makes it possible to predict the gross formula of algae. A high content of sulfur, nitrogen and oxygen reduces the quality of fuel, increasing its corrosiveness, reducing calorific value, and changing consumer properties. In this regard, further studies were aimed to estimate the element composition of algae. The research results are presented in Table 6.  
The ash content of biomass samples was measured in conjunction with elemental analysis, and the results are shown in Table 7. The ash content was estimated gravimetrically by calcining the biomass in a muffle cabinet at a temperature of 900 \({}^{\circ}\)C.
# Tumor Volume Distributions Based on Weibull Distributions of Maximum Tumor Diameters
## 3 Results
### Tumor Volume Distributions Determined from SEER for Breast, NSCLC and Liver  
In Figures 4a, 5a and 6a, the histograms of the tumor diameter data from the SEER database with the corresponding Weibull distribution fits are shown. The symbols represent the SEER data and the solid lines the respective fits. As expected, the critical diagnostic limit diameter \(d_{c}\) for all three tumor types decreases with increasing diagnostic period. Table 2 provides an overview of these results. The dashed line shows the intrinsic Weibull distribution of tumor diameters according to Equation (1).  
Figure 3: Fit (solid line) of the normalized modified Weibull distribution of Equation (1) to the histogrammed tumor diameters (symbols) of 176 NSCLC tumors of the Cancer Image Archive in (**a**). The resulting fit parameters are \(\lambda\) = 0.13 \(\pm\) 0.01, k = 2.14 \(\pm\) 0.36 and \(d_{c}\) = 1.5 \(\pm\) 0.9 cm. In (**b**) the histogrammed volume data are plotted together with volume distribution from Equation (5). It should be noted that the volume distribution was obtained by using the parameters of the diameter fit. The vertical bars represent the statistical error sqrt(n)/n, where n is the number of subjects in the corresponding diameter and volume bin, respectively.  
Figure 2: Fit of the tumor volumes to maximum tumor diameters of 176 NSCLC tumors of the Cancer Image Archive (depicted as ”+”). The relation \(\frac{\pi}{6}\cdot d^{k}\) is shown as the solid line with \(k\) = 2.21 obtained from a least-squares fit.  
Figure 4: Fit of the normalized modified Weibull distributions of Equation (1) to the histogrammed tumor diameters for breast cancers from the SEER database for different time periods in (**a**). In (**b**), the obtained volume distributions from Equation (5) are shown. The dashed lines show the intrinsic size distributions.  
Figure 5: Fit of the normalized modified Weibull distributions of Equation (1) to the histogrammed tumor diameters for NSCLC from the SEER database for different time periods in (**a**). In (**b**), the obtained volume distributions from Equation (5) are shown. The dashed lines show the intrinsic size distributions.  
Figure 6: Fit of the normalized modified Weibull distributions of Equation (1) to the histogrammed tumor diameters for liver cancers from the SEER database for different time periods in (**a**). In (**b**), the obtained volume distributions from Equation (5) are shown. The dashed lines show the intrinsic size distributions.  
After \(d_{c}\), \(\lambda\) and \(k\) were determined from the diameter distributions, \(V_{c}\) and \(\overline{V}\) were calculated using Equations (2) and (5), respectively (see Table 2). With this information, the exponential distribution of volumes can now be calculated using Equation (6). The resulting distributions and the critical limit volumes \(V_{c}\) are shown in Figures 4b, 5b and 6b for breast, NSCLC and liver, respectively. The dashed line again represents the intrinsic distribution of the volumes, which is exponential when \(V_{c}\) = 0.
# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 2 Related Work
### Street Network Modeling of Crime
GLDNet utilized a graph-based representation of network-structured data and incorporated a localized diffusion network to effectively model spatial propagation [10]. With the introduction of graph representation learning methods into crime research, it became possible to simultaneously learn street attributes and the structural features of street networks. For instance, [PERSON] et al. applied Deepwalk to derive the topological structure embeddings of a street network called SN2V, and they obtained the embeddings of a heterogeneous information network through link prediction tasks called HIN2V and fed both embeddings into the DeepFM model for street-level crime prediction [11]. A model based on GCN named as DSTGCN was proposed by [PERSON] et al. [12]. The model performs graph convolutional operations on spatial information combined with dynamic variations in temporal and external perspective for traffic accident prediction at the street level. However, such methods generally assign equal weight to all the neighboring nodes without accounting for the varying contributions of different nodes during the aggregation and propagation of information. In practical applications, real-world graphs frequently exhibit noise through the presence of connections between different nodes. This can result in acquiring suboptimal representations due to their failure to distinguish between nodes within neighborhoods [43]. Addressing the biases in crime prediction related to weight allocation within the street network remains a critical challenge. The introduction of graph attention networks (GATs) offers a promising solution, as its self-attention mechanism enables the learning of node features from multiple perspectives, enhancing the richness and robustness of the feature representations [44]. Additionally, the GAT's ability to dynamically capture the relationships between nodes, rather than relying solely on static graph structures or initial node features, is particularly relevant for real-time incident prediction. These advancements highlight the potential of a GAT to significantly improve predictive accuracy and provide deeper insights into the spatial dynamics of urban crime.
## 3 Historical Review to State-of-the-Art
### Erbe
Reproduced and adapted from _Satellite Meteorology: An Introduction_, [PERSON], p. 466, Copyright (1995) [35] after [PERSON] et al. [21]. When applying ERBE ADMs, supporting observations to identify the scene type associated with each ERBE radiance observation, such as those used with Nimbus VII in the generation of the ERBE ADMs, were not available. Instead, a maximum likelihood estimation approach [36] was employed that relied only on the longwave and shortwave radiances observed by ERBE (Figure 5). The general idea was that as the cloud cover increases, the scene becomes brighter, increasing the shortwave reflectance, while the effective emitting surface is shifted higher in the atmosphere and therefore to colder temperatures, reducing the longwave emission. While this only permits a relatively simple scene identification by today's standards, the simplicity of the ERBE approach had the advantage of not relying on a coincident imager. For each of the ERBE scene types, 10 solar zenith angle bins were considered. Each of those were then further split into 7 viewing zenith angle bins and 8 relative azimuth angle bins (Figure 6a). The majority of angular bins for each scene type were populated with the minimum of eight Nimbus VII ERB scanner observations that were determined appropriate for a reliable angular bin average [21]. One major issue was that, due to the sun-synchronous orbit of Nimbus VII, some scenes were never observed at certain solar zenith angles. As such, all viewing zenith angle and relative azimuth angle bins for a given solar zenith angle bin over some scene types were missing. This issue was addressed with the Helmholtz reciprocity principle [38], which essentially says that the reflective characteristics of a scene (in this case, the anisotropic factors) are independent of the direction that the radiation is travelling. If this principle holds, solar zenith angle and viewing zenith angle can be interchanged. Apart from two solar zenith angle bins over the desert that were filled with an empirical model [39], any remaining missing angular bins after applying this principle were infrequent and filled with bi-linear interpolation or extrapolation.
# On the Virtual Cell Transmission in Ultra Dense Networks
## 3 Transmission Schemes Based on Virtual Cell Merging
### Virtual Cell Merging with IA Transmission
#### 3.2.1 Virtual Cell Merging  
Assume that \(\gamma_{k,\mathcal{K}\setminus k}\) denotes user \(k\)'s interference strength list. Find user \(k\) and user \(j\) that have the strongest interference with each other as:  
\[\{k,j\}=\{k^{\prime},j^{\prime}\left|\text{max}(\gamma_{k^{\prime},j^{\prime} })\right.\} \tag{18}\]  
Then, find the third user \(q\) that has the strongest interference with user \(k\) and user \(j\) as  
\[\{q\}=\{q^{\prime}\left|\text{max}(\gamma_{k,q^{\prime}}+\gamma_{j,q^{\prime} })\right.\} \tag{19}\]  
Then, a virtual cell cluster is formed with the three strongest interfering virtual cells, where the user set is denoted as \(\mathcal{K}_{m}=\{j,k,q\}\) and the RBA set is denoted as \(\mathcal{B}_{m}=\mathcal{V}_{k}\cup\mathcal{V}_{j}\cup\mathcal{V}_{q}\). Update the ungrouped user set and RBA set as \(\mathcal{K}=\mathcal{K}\setminus\mathcal{K}_{m}\) and \(\mathcal{B}=\mathcal{B}\setminus\mathcal{B}_{m}\), respectively, and repeat the above steps until \(\mathcal{K}\) is empty. The details of this virtual cell merging algorithm are presented in Algorithm 2.