esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 2 Materials and Methods
### Data Analysis
#### 2.3.3 Cross-Validation  
Five-fold cross-validation was used to develop the yield estimation model for each dataset. Cross-validation was implemented using the _Caret_ package [36] in R statistical software [31], repeated ten times for model robustness. The model produced 'fitted values', which represented the estimated yield (in grams). Following this, the estimated yield (g) values were linearly regressed against the  
Figure 8: (a) The Kinect point cloud for an LR-treated vine (1E—east side) and (b) the segmented point cloud of the same vine.  
actual mass (g) to produce a final r\({}^{2}\) value (coefficient of determination), indicating the potential for yield estimation. The Root Mean Square Error (RMSE) was computed from the linear regression, indicating the yield estimation error (in grams).
# Optimization of Heat Pump Systems in Buildings by Minimizing Costs and CO\({}_{2}\) Emissions
## 2 Methodology
The total amount of heat produced is determined by the capacity (\(q_{ijk}\)), the number of operating hours (\(y_{ijk}\)), and the quantity (\(x_{i}\)) of the selected HP/EH \(i\) in month \(j\) of year \(k\). The selected heating system must not only satisfy the actual monthly heat demand but also the maximum heat demand per hour. Constrains (2) ensure that the total capacity of the selected heating system is not less than the maximum heat demand per hour (\(Q_{maxj}\)) for each month in the heating season of the first year. In this case, the total system capacity for each analyzed month is calculated using the heating capacities (\(q_{ij1}\)) of technology \(i\) in each analyzed month \(j\) of the first year. Free terms (\(Q_{maxj}\)) of the constraints are the maximum heat demand per hour for each month \(j\) or the maximum required heating system capacity, which is also calculated only for the first year when the heat demand is the highest. The problem constraint, which states that the HPs must satisfy the majority of the maximum heat demand per hour (\(Q_{maxj}\)), is implemented by Equation (3). In the inequality (3), \(s\) is the number of HPs included in the analysis, and \(p\) is the coefficient indicating the proportion of the maximum heat demand per hour that must be covered by the heat pumps (\(0\leq p\leq 1\)). Additionally, the designed HP heating system cannot exceed the area of the heating substation (Equation (4)). In the constraint (4), \(s_{i}\) denotes the area of HP \(i\) (m\({}^{2}\)), and \(S_{hp}\) is the area of the heating substation (m\({}^{2}\)). The multi-objective optimization problem (Equations (1)-(6)) is solved using a goal-programming and weighting coefficients method. The goal-programming method transforms a problem with two objective functions (Equations (1) and (2)) into a problem with one objective function:  
\[\text{minimize}\ \alpha_{1}d_{1}^{+}+\alpha_{2}d_{2}^{+}, \tag{9}\]  
by introducing additional non-negative variables, called deviations (\(d_{1}^{-},d_{1}^{+},d_{2}^{-},d_{2}^{+}\)), in objectives demonstrated in Equations (1) and (2):  
\[\sum_{i=1}^{n}\sum_{j=1}^{t}\sum_{k=1}^{m}c_{ijk}y_{ijk}x_{i}+\sum_{i=1}^{n}( \widetilde{c}_{i}+\hat{c}_{i}+m\hat{c}_{i})x_{i}+d_{1}^{-}-d_{1}^{+}=0, \tag{10}\]  
\[\sum_{i=1}^{n}\sum_{j=1}^{t}\sum_{k=1}^{m}\widetilde{c}_{ijk}y_{ijk}x_{i}+d_{ 2}^{-}-d_{2}^{+}=0, \tag{11}\]  
here, \(\alpha_{1},\alpha_{2}\) are positive weights, which reflect the importance of the individual objectives.
## 4 Methods for EIT Data Reduction and CNN Model Compression for Efficient Edge Deployment
### Filter Pruning  
Model pruning is a technique to reduce the complexity and size of artificial intelligence models without significantly compromising their performance. It involves identifying and removing redundant or unimportant model components, such as neurons or weights. Common criteria for pruning include magnitude-based (removing weights close to zero), sensitivity-based (evaluating the impact of removing components on model performance), and structure-based (pruning entire layers). By carefully applying pruning techniques, it is possible to obtain lightweight models that maintain a comparable performance to their larger counterparts.  
In a CNN, filter-level pruning entails deleting certain filters and the feature maps that go along with them, changing the network architecture. This kind of pruning reduces the overall amount of Floating-Point operations (FLOPs) needed for inference in a convolutionalnetwork [32; 33]. The FLOPs of the overall network reduce quadratically when many layered convolutional layers are pruned, making this reduction especially noteworthy [34].  
Consider a CNN with n convolutional layers and m fully linked layers, as shown in (2). The first inference cost can be expressed as follows in terms of FLOPs:  
\[C_{i}=\sum_{i=1}^{n}u_{i}+\sum_{i=1}^{m}v_{i} \tag{2}\]  
Here, \(u_{i}\) represents the FLOPs of convolutional layer \(i\) and \(v_{i}\) the FLOPs of the fully connected layer \(i\). After pruning, where \(o_{i}\) denotes the initial number of filters in the convolution layer \(i\) and \(r_{i}\) the number of filters remaining after pruning, the inference cost of the pruned network is recalculated as presented in (3):  
\[C_{p}=\frac{r_{1}}{o_{1}}u_{1}+\sum_{i=2}^{n}\frac{r_{i-1}r_{i}}{o_{i-1}o_{i}}u _{i}+\frac{r_{n}}{o_{n}}v_{1}+\sum_{i=2}^{m}v_{i} \tag{3}\]  
This formula shows that reducing the number of filters in one layer has a cumulative effect on the FLOPs in subsequent layers, significantly reducing the overall computational cost of the network.
###### Abstract  
In this study, we evaluated the invasion of bacteria into the dentinal tubules of retrofilled extracted human teeth, and the influence of different fluorescently labeled retrograde filling materials on the bacterial invasion and viability, by means of confocal laser scanning microscopy (CLSM). The root apices of extracted teeth were cut, prepared, and filled retrogradely using either intermediate restorative material (IRM), mineral trioxide aggregate (MTA), or Biodentine. The roots were filled with _Enterococcus faecalis_ bacteria from their coronal part for 21 days. Then, 3-mm-long apical segments were cut to get root axial slices, and the bacteria were fluorescently stained and evaluated by CLSM. Bacterial penetration into the dentinal tubules favored the bucco-lingual directions. The filling materials penetrated up to 957 \(\upmu\)m into the tubuli, and the bacteria, up to 1480 \(\upmu\)m (means: 130 and 167 \(\upmu\)m, respectively). Biodentine fillings penetrated less and the associated bacteria penetrated deeper into the tubuli compared to MTA or IRM (\(p\) = 0.004). Deeper filling penetration was associated with shallower penetration of both dead and live, or live alone, bacteria (\(p\) = 0.015). In conclusion, the current study enables better understanding of the microbiological-pathological course after endodontic surgical procedures. It was found that even with retrograde fillings, bacteria invade deep into the dental tubules, where deeper filling penetration prevents deeper penetration of the bacteria and adversely affects the viability of the bacteria.  
retrograde filling; _Enterococcus faecalis_; bacterial penetration; bacterial invasion; root canal; confocal microscopy; Biodentine; MTA; IRM; CLSM
## 3 Cross-Layer Controller Tasking Scheme  
This section details the proposed algorithm, Cross-Layer Controller Tasking Scheme (CLCTS), designed to reduce task acceptance error (TAE) through task grouping, re-tasking, and cross-layer controller coordination in IIoT environments.  
Monotonous job completion in smart industries aided by automation through edge devices and remote assistance is limited. Task offloading and prolonged executions are inevitable due to asynchronous task types. The problem is addressed using task offloading, scheduling, prioritizing, and multiple allocations, as discussed above. The initial differentiation of tasks based on their type, processing time, and resource utilization can be grouped to a certain extent to reduce task acceptance errors. The proposed scheme utilizes this limited certainty to ensure better task completion by reducing task acceptance rates. For this purpose, neural learning is used by dividing the operation phases based on different intervals and edge nodes for performance-centric decisions.
# Some New Bounds for the Inverse Sum Indeg Energy of Graphs
## 3 Bounds for the ISI Spectral Radius  
In this part, we establish several bounds for the ISI spectral radius \(\mu_{1}\) of graphs.  
We first present an upper bound on \(\mu_{1}\) in terms of the maximum degree \(\Delta\), minimum degree \(\delta\), order \(n\) and size \(m\).  
**Theorem 1**.: _If \(G\) is an \((n,m,\delta,\Delta)\)-graph so that \(\Delta\geq\delta\geq 1\), then_  
\[\mu_{1}\leq\frac{\Delta}{2}\sqrt{2m-\delta(n-1)+(\delta-1)\Delta}\;\;, \tag{15}\]  
_the equality is acquired iff \(G\) is a regular graph._  
Proof.: Lemma 1 deduce that \(\mathcal{C}\leq\frac{\Delta}{2}A(G)\). Furthermore, by Lemmas 6 and 11, we have  
\[\mu_{1}\leq\frac{\Delta}{2}\lambda_{1}\leq\frac{\Delta}{2}\sqrt{\Delta(\delta -1)+2m-(n-1)\delta}.\]  
If \(\mu_{1}=\frac{\Delta}{2}\sqrt{\Delta(\delta-1)+2m-(n-1)\delta}\), from \(\mathcal{C}=\frac{\Delta}{2}A(G)\), we have \(\frac{d_{i}d_{j}}{d_{i}+d_{j}}=\frac{\Delta}{2}\) i.e., \(d_{1}=d_{2}=\ldots=d_{n}=\Delta\). Therefore, \(G\) must be regular.  
On the contrary, it is obvious that the equality in (15) holds when \(G\) is regular.  
This completes the proof.  
**Theorem 2**.: _For any \((n,\delta)\)-graph \(G\),_  
\[\mu_{1}\geq\frac{\delta}{2}\sqrt{\frac{M_{1}}{n}}\;, \tag{16}\]  
_the equality is acquired iff \(G\) is regular._  
Proof.: From Lemma 1, we know that \(\frac{d_{i}d_{j}}{d_{i}+d_{j}}\geq\frac{\delta}{2}\). Then, \(\mathcal{C}\geq\frac{\delta}{2}A\). Furthermore, by Lemmas 6 and 8,  
\[\mu_{1}\geq\frac{\delta}{2}\lambda_{1}\geq\frac{\delta}{2}\sqrt{\frac{\sum_{i= 1}^{n}d_{i}^{2}}{n}}=\frac{\delta}{2}\sqrt{\frac{M_{1}}{n}}\;.\]  
Now, let's assume that the equation holds in (16). Then aforementioned inequalities must be equalities. From \(\mathcal{C}=\frac{\delta}{2}A\), we have \(\frac{d_{i}d_{j}}{d_{i}+d_{j}}=\frac{\delta}{2}\) i.e., \(d_{1}=d_{2}=\ldots=d_{n}=\delta\). Thus, \(G\) must be regular.  
On the contrary, when \(G\) is regular, it can be easily proved that \(\mu_{1}=\frac{\delta}{2}\sqrt{\frac{M_{1}}{n}}\).  
**Theorem 3**.: _For any \((n,\Delta)\)-graph \(G\),_  
\[\mu_{1}\geq\frac{M_{2}}{n\Delta}, \tag{17}\]  
_the equality is acquired iff \(G\) is regular._  
Proof.: Let \(\mathbf{x}=(x_{1},x_{2},\ldots,x_{n})^{T}\) be a unit vector, \(x_{i}\in\mathbb{R}\)\((1\leq i\leq n)\). Then,  
\[\mathbf{x}^{T}\mathcal{C}\mathbf{x}=2\sum_{v_{i}v_{j}\in\mathcal{E}(G)}\frac{ d_{i}d_{j}}{d_{i}+d_{j}}x_{i}x_{j}\geq 2\sum_{v_{i}v_{j}\in\mathcal{E}(G)}\frac{ d_{i}d_{j}}{2\Delta}x_{i}x_{j}\;. \tag{18}\]
# A 11 mW 2.4 GHz 0.18 \(\upmu\)m CMOS Transceivers for Wireless Sensor Networks
## 3 Circuit Implementation
### System Level Integration  
In addition to the key building blocks, there are also some other circuits in the proposed system. The most important part is serial peripheral interface (SPI) circuit, since many circuits in the system are configurable. For example, the transceiver can be configured as an RX or TX, and the gain of the RX is programmable. In addition, by changing the division ratios, which are equal to 2 \(\times\) (52 + M), the output frequency of the PLL is configurable to support different channels. Accordingly, the working bands of the voltage controlled oscillator should be adjusted to support the desired operating frequencies. The SPI occupies a silicon area of 240 \(\upmu\)m \(\times\) 80 \(\upmu\)m (0.0192 mm\({}^{2}\)). The SPI has 32 bits, while 24 of them are used to control the transceiver. D0-D2 are used to control the cap array. D3-D4 control the frequency division of the integer divider. D5-D14 control the frequency division of the fractional divider. D15 is the enable bit of PLL. D16-D19 are used to switch the gain of the IF amplifier. D20 enables the receiver. D21 enables the power amplifier. D22 enables FSK modulate of the transmitter. D23 enables FM modulate of the transmitter. By designing SPI circuit, all of the control bits can be set by several on-chip registers.  
Finally, as a complete chip, it includes some functional building blocks, such as the biasing circuits, crystal buffer, and decoupling capacitors. The crystal buffer is designed to support an off-chip crystal of 20 MHz (to meet 2.4 GHz PLL output frequency range). De-coupling capacitors between the supply voltage and ground are added (as many as possible) to improve the noise performance.  
Figure 11: The schematic level simulation result of the PLL.  
Figure 10: The topology of the (**a**) PFD, (**b**) CP, and (**c**) loop filter.  
Implemented using a 0.18 \(\upmu\)m CMOS technology, the layout of the transceiver is about 0.7 mm \(\times\) 0.7 mm and the full chip is about 1.1 mm \(\times\) 1.1 mm with the added capacitors and ESD pads.
# An Eight-Channel C-Band Demux Based on Multicore Photonic Crystal Fiber
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\lambda_{\text{m}}\) (nm) & **1530** & **1535** & **1540** & **1545** & **1550** & **1555** & **1560** & **1565** \\ \hline n\({}_{\text{Si},\text{N}_{4}}\) & 1.9968 & 1.9967 & 1.9966 & 1.9964 & 1.9963 & 1.9961 & 1.996 & 1.9959 \\ n\({}_{\text{Silica}}\) & 1.4443 & 1.4442 & 1.4441 & 1.4441 & 1.4444 & 1.444 & 1.4439 & 1.4438 \\ n\({}_{\text{LiNbO}_{3}}\) & 2.1481 & 2.148 & 2.1478 & 2.1477 & 2.1476 & 2.1474 & 2.1473 & 2.1471 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The multicore photonic crystal fiber (PCF) materials’ reflective index values. Figure 1: Refractive index profile of the 1 \(\times\) 8 wavelength demux: (**a**) XZ plane at y = 0 cm. (**b**) XY plane at z = 0 cm. (**c**) XY plane at z = 5 cm. The three LiNbO\({}_{3}\) layers were used to function as the selected waveguide switch that controls the light propagation direction along the fiber length. The first selected switch is located at the Z-axis range between 2.03-2.5 cm, and can demultiplex the light propagation between ports 1, 5, 7, and 3, and ports 8, 4, 2, and 6. The second selected switch is located at the Z-axis range between 3.01-3.445 cm, and can demultiplex the light propagation between ports 8 and 4, and ports 2 and 6. The third selected switch is located at the Z-axis range between 3.46-4 cm, and can demultiplex the light propagation between ports 3 and 7 and ports 5 and 1. It is important to emphasize that the coupling length of the resonated light between two closer ports is dependent on the key geometrical parameters (d, \(\Lambda\)) and the operating wavelength. Thus, the coupling length can be found using the equation below [29; 30]:  
\[\mathrm{L}^{\Lambda}_{\mathrm{Coupling}}\ =\ \frac{\pi}{\mathrm{k}_{0}\big{(} \mathrm{n}_{\mathrm{symmetric}}(\mathrm{d},\Lambda,\Lambda)-\mathrm{n}_{ \mathrm{anti-symmetric}}(\mathrm{d},\Lambda,\Lambda)\big{)}} \tag{1}\]  
where \(\mathrm{k}_{0}\) is the free-space wave vector, \(\lambda\) is the operating wavelength, and \(\mathrm{n}_{\mathrm{anti-symmetric}}\) and \(\mathrm{n}_{\mathrm{symmetric}}\) are the anti-symmetric and symmetric effective refractive index, respectively. It is worth mentioning that the coupling length is periodical due to the oscillation between two closer ports.
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Rubber Plantation Mapping and Accuracy Assessment
For example, some farmland was misidentified as rubber plantation by the pixel-based RF/SVM approaches, as shown in Figure 12(A-3,A-4). However, compared to object-based RF approaches, pixel-based methods have obvious advantages when identifying forest paths or small roads. The object-based RF approach displayed the worst performance in this context, as shown in Figure 12(B-5), and the object-based SVM approach yielded the best performance (Figure 12(B-6)). In this study, although the object-oriented RF and SVM produced the same classification accuracy based on the evaluation of the confusion matrix, the object-based SVM classifier was superior to the object-based RF classifier in some tasks, such as forest path recognition and plot boundary forest recognition. The selection of validation samples is crucial for evaluating the accuracy of classification results, and the applied approach should be verified in different research areas and with more sample sets in the future. Figure 12: Comparison of rubber plantation maps at two sites derived based on different classification methods. **(A-1,A-2)** show the PlanetScope images taken at Site A on 8 June 2019, and 27 February 2019, and are combinations of B4, B3, and B2; **(A-3,A-4)** are the classification results obtained through pixel-based RF approach and pixel-based SVM approach, respectively; **(A-5,A-6)** are the classification results derived using the object-based RF approach and the object-based SVM approach, respectively; for the images in **(B-1–B-6)** see above. **(A-3–A-6)** and **(B-3–B-6)** were superimposed on the image obtained on 8 June 2019.
# An Iterative Closest Point Method for Lidar Odometry with Fused Semantic Features
## 3 Overview of the System  
The LO algorithm system we propose consists of three parts: the first part preprocesses the point cloud obtained by Lidar, including point cloud filtering and semantic segmentation. Specific details are provided in Sections 4.1 and 4.2. The utilization of semantic information in the point cloud is a key focus of this paper. The second part involves extracting feature points through curvature and semantic labels, including plane points and edge points, as detailed in Section 4.3. Subsequently, feature matching, incorporating semantic information, is employed to obtain a relatively rough pose (composed of a rotation matrix R and translation vector t, describing the spatial relationship in three-dimensional space), as described in Section 4.4. Feature matching in this part is performed frame-to-frame, meaning that feature points of the current frame can only be matched with those of the previous frame, resulting in larger pose errors. The third part uses the pose obtained in the second part as prior information for one round of semantic label-assisted frame-to-submap feature matching. Unlike some classical LO algorithms, in this paper, we propose a LO algorithm framework that integrates semantic features, as shown in Figure 1. It is noteworthy that we have designed different semantic-assisted methods for frame-to-frame feature matching and frame-to-submap feature matching (in frame-to-submap, during point-to-plane feature matching, principal component analysis is used to fit the plane, instead of merely finding three nearest points as the plane), despite their similar names.
# Edge Federated Optimization for Heterogeneous Data
## 5 Experimental Results
### Performance Evaluation
#### 5.2.5 FedAvg/FedProx with Strategy 5  
To further enhance the model's performance, we subsequently modified the open-source code DessiLB to implement a structured pruning strategy, conducting experiments with multiple pruning percentages (i.e., 10% (Figure 10(a)), 20% (Figure 10(b)), 40% (Figure 10(c)), and 80% (Figure 10(d)) pruning percentages). Based on the experimental results in Figure 11, we observed that the FedAvg with structured pruning has performance deterioration compared to the classification performance of the FedAvg with unstructured pruning.  
Figure 11: Confusion matrix comparison of model performance: (**a**) at 10%, (**b**) at 20%, (**c**) at 40%, and (**d**) at 80% structured pruning rates.  
On the other hand, at a pruning rate of 10%, the FedProx with structured pruning achieved the best F1 scores, reaching 96.01% and 77.81% (Figure 11a), respectively, which is superior to those of the FedProx with unstructured pruning, reaching 95.58% and 63.47% (Figure 10a), respectively. However, with an increasing pruning percentage, the F1 scores exhibited a decreasing trend, reaching a minimum at the 40% pruning rate, followed by a subsequent rising trend. This phenomenon may be attributed to the substantial pruning of nodes, leading the model to undergo a near-retraining process, thereby highlighting the impact of structured pruning on model performance within specific contexts.  
To assess the training efficiency, Figure 12 depicts the F1 score with respect to the number of epochs using the FedProx algorithm with Strategy 5 at a pruning rate of 10% and an early stopping mechanism. Note that in this typical run, with the pruning operation, the model size and parameters are reduced by removing connections, leading to improved generalization. Moreover, given that the training process consists of three rounds with 10 epochs at each round. Observe that Figure 12 provides an example where the training process with the proposed approach stops 12 epochs earlier than that without early stopping (which stops with a 30 epoch delay). Accordingly, the training efficiency of F1 has improved. The series of experimental results underscores the sensitivity of structured pruning to model performance at different pruning levels, providing valuable insights for further exploration of optimized pruning strategies.