esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Accuracy and Efficiency Verification of the Calculation Method
### Three-Dimensional Model  
In addition, we calculated a three-dimensional model with a chargeable anomaly and compared the result with Reference [30]. The dimensions of the cell mesh were \(70\times 70\times 70\), and the minimum grid size was \(25\) m. The modeling volume was \(3500\ \times 3500\ \times 3500\ \mathrm{m}^{3}\). The size of the chargeable anomaly was \(100\times 100\times 80\ \mathrm{m}^{3}\), while the distance from the top to the bottom of the chargeable anomaly was \(40\) m. The parameters of the Cole-Cole model were \(\sigma_{\infty}\)= \(0.1\) S/m, \(\tau\)= \(0.1\) s, \(\eta\)= \(0.3\), and c = \(0.5\). The background conductivity was \(\sigma_{\infty}\)= \(0.001\) S/m. A vertical point dipole emitter, which was located \(30\) m above the surface, was used, and the receiver and transmitter were at the same location. The calculation results are shown in Figure 7, from which it can been seen that the two IP effect curves were consistent. The relative error was less than \(10\%\), which further verifies the correctness of the method.  
In terms of computation load, the iterations in the fictitious wave field are as follows:  
\[N_{t}=\frac{\sqrt{3}R_{\mathrm{max}}}{\Delta\mathrm{x}}\times\sqrt{\frac{ \sigma_{\mathrm{max}}}{\sigma_{\mathrm{min}}}}, \tag{37}\]  
where \(N_{t}\) represents the number of iteration steps, \(R_{\mathrm{max}}\) is the maximum transceiver distance, \(\sigma_{\mathrm{max}}\) and \(\sigma_{\mathrm{min}}\) are the maximum and minimum conductivity, respectively, and \(\Delta x\) is the minimum grid size. Using Equation (37), the number of iteration steps was \(76\),\(681\). It should be noted that only  
Figure 5: Influence of conductivity at infinite frequency on IP effect.  
Figure 6: Influence of chargeability on IP effect.  
one transmitter rather than an array was selected in this work. Compared with Reference [30], the calculation efficiency was improved by about five-fold.
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 3 Methodology
### Algorithm and Optimization  
In the following part, Algorithm 1 is presented for the training TD-LSTM networks at an observation point. We first constructed the training instances from the original observations. Then, TD-LSTM was trained via back-propagation [21] and optimized by Adaptive Moment Estimation (Adam). Adam is an adaptive-learning rate-optimization algorithm. Its advantage mainly lies in that, after bigotry correction, the learning rate of each iteration has a certain range, which makes the parameters relatively stable [22].  
```
1: historical observations:\(\{X_{1},...,X_{n-1}\}\);
2: target at time \(t\): \(X_{t}\);
3: lengths of closeness, period, trend: \(l_{c},l_{p}\);
4: period: \(p\);
5: TD-LSTM model \(\mathcal{M}\); //construct training instance
6:\(\mathcal{D}\leftarrow\varnothing\);
7:for all available time interval \(t(1\leq t\leq n-1)\)do
8:\(S_{c}=[X_{t-l_{c}},X_{t-(l_{c}-1)},...,X_{t-1}]\);
9:\(S_{p}=[X_{t-l_{p}},X_{t-(l_{p}-1)},...,X_{t-p}]\);
10: put a training instance \(\big{(}\{S_{c},S_{p}\}\big{)}\) into \(\mathcal{D}\);
11:endfor // train model
12: initialize parameter \(\theta\);
13:repeat
14: randomly select a batch of instances from \(\mathcal{D}\);
15: find \(\theta\) by optimization algorithm;
16:until the objective is minimized
17: output the optimized TD-LSTM model \(\mathcal{M}\);
```  
**Algorithm 1** Training of TD-LSTM networks
## 5 Experiments
### Ablation Studies and Parameter Studies
#### 5.4.1 Ablation Studies  
To elucidate the significance of various aggregation modules within DAGA, ablation experiments are conducted on the Weeplaces dataset utilizing variants of DAGA with distinct aggregation strategies: _Maxpooling_ and _Meanpooling_ in lieu of the default _Attention_ mechanism. The performance metrics for the DAGA variants, namely, DAGA-Max, DAGA-Mean and the standard DAGA-Attention, are summarized in Table 5.  
The empirical results reveal that DAGA-Attention, incorporating the attention aggregator, consistently outperforms the other variants. This finding underscores the superior capability of the attention aggregator in effectively synthesizing individual member preferences into a cohesive group representation, thereby enhancing recommendation accuracy. In contrast, DAGA-Max, which solely relies on the maximal elements within the member representations, exhibits the lowest performance, highlighting the limitations of such an approach in capturing the full spectrum of group preferences. DAGA-Mean demonstrates moderate performance, indicating that averaging member representations, while more comprehensive than Maxpooling, still lacks the nuanced adaptability afforded by the attention aggregation.  
In order to dissect the performance of our proposed multi-head disentangled self-attention module, we further conduct experiments with different structure settings. To be specific, multi-head disentangled self-attention encoders with 1-3 layers and with 2, 4 and 8 heads, respectively, are verified. When we test one of them, the other structure settings are kept as default (two layers, four heads with attention aggregator).  
The experimental results demonstrate that the effect of the layer setting is less important than that of the head setting. The multi-head disentangled self-attention module with two attention layers and four heads can produce the best performance, which indicates  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Settings** & **Model** & **N@20** & **N@50** & **R@20** & **R@50** \\ \hline \multicolumn{6}{c}{**Aggregator**} \\ \hline \multirow{3}{*}{2-Layer with 4-Head} & DAGA-Max & 0.486 & 0.514 & 0.699 & 0.791 \\  & DAGA-Mean & 0.498 & 0.558 & 0.715 & 0.808 \\  & DAGA-Attention & **0.512** & **0.576** & **0.723** & **0.814** \\ \hline \multicolumn{6}{c}{**Multi-Head Disentangled Self-Attention Encoder**} \\ \hline \multirow{3}{*}{4-Head with Att. Aggre.} & 1-Layer & 0.493 & 0.539 & 0.713 & 0.802 \\  & 2-Layer & **0.512** & **0.576** & **0.723** & **0.814** \\  & 3-Layer & 0.510 & 0.565 & 0.720 & 0.809 \\ \hline \multirow{3}{*}{2-Layer with Att. Aggre.} & 2-Head & 0.505 & 0.548 & 0.715 & 0.805 \\  & 4-Head & **0.512** & **0.576** & **0.723** & **0.814** \\ \cline{1-1}  & 8-Head & 0.508 & 0.570 & 0.718 & 0.810 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study: performance of DAGA variants on Weeplaces dataset.  
that the preference relationships between users are relatively easy to capture, and the more complex model structures are not necessary.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 4 Results
### Forecast Visualization  
Each model employs a unique approach to learning patterns from the data. Linear regression relies on a simple linear relationship between input features and the target variable. Decision tree uses a hierarchical, rule-based structure to split data based on feature values. Random forest builds on the decision tree concept by aggregating multiple trees to improve prediction accuracy and reduce overfitting. Lastly, the MLP (multilayer perceptron) is a type of neural network that captures complex, nonlinear relationships through layers of interconnected nodes. Figures 1-4 illustrate the structures of these models in detail. Figure 5 is a training loss trend chart of the four models in 50 training cycles. The training progress chart shows that the MLP model achieved the lowest training loss and converged the fastest among all models, indicating strong learning efficiency. Linear regression also demonstrated steady and consistent improvement, ending with relatively low training loss. The decision tree model showed moderate performance, with a gradual decrease in loss over time. In contrast, the random forest maintained higher training loss throughout, likely due to its ensemble structure, which emphasizes generalization rather than closely fitting the training data. Overall, MLP outperformed the others during training, followed by linear regression, decision tree, and random forest.
## 4 Discussion
### Crop Coefficients (Kc) Derived from NDVI: Relevance of Kc Variability
This dynamic approach to irrigation management is especially beneficial in regions facing water scarcity, where efficient water use is critical for sustaining crop health and productivity. The Kc values derived from the NDVI in this study demonstrated their usefulness in capturing the water demand of the avocado crop, providing an adaptive tool for precision irrigation. These values were closely aligned with the phenological stages of the crop, highlighting the relationship between vegetation growth and water needs. The ability to adjust irrigation schedules based on real-time Kc estimates derived from remote sensing data represents a substantial advancement in precision agriculture, particularly for crops like avocados, which are highly sensitive to water stress.
# Required Evidence for Clinical Applications of Liquid Biopsy Using Especially CTCs in Lung Cancer
## 2 Tumor Tissue Obtained from Primary Tumors or Metastases  
For optimal treatment decision-making, the histological classification, the presence of targetable mutations, immune cells and surface molecules (e.g., PD-L1) are important. This information is routinely obtained using formaline-fixed, paraffine-embedded tissue blocks from tumor biopsies. However, about 20%-25% of endoscopic biopsies do not provide enough tumor cells to perform molecular predictive testing or the DNA is of low quality [10]. Sometimes they do not even contain enough tumor cells for a well-established histopathological examination. Additionally, biopsies are invasive for the patient and not without complications.
## 6 Experiments
### Partial Experiments
#### 6.1.2 Dynamic Firefighter Guiding
When the measurements reached the Base Station, it recomputed the escape paths. The new local guiding information was flooded. The resulting guiding information stored in the nodes after the flood at \(t_{3}\) is in Figure 11(e). Figure 11(a-c) shows with asterisks the resulting optimal path for room 1 in the three conditions. In \(t_{1}\) the optimal path was {2, 3, 6, 0}. In \(t_{2}\) it was {2, 5, 7, 6, 0} and in \(t_{3}\) it was {2, 5, 7, 9, 12, 14, 0}. The experiment was repeated 10 times with random fire conditions, and at all times the method performed as expected. The method is rather robust to losses of _EscapeRequest_ and _EscapeResp_ packets. Temporal WSN disconnections can involve loss of updated fire measurements at the Base Station, which can result in unsuitable escape paths. Frequent transmission of _FireSense_ packets help preventing these cases. Also, CTP re-computes multi-path routes dynamically to reduce the impact of node failures and unstable radio links. WSN disconnections can involve loss of local guiding information by static nodes. For instance, the interaction between updated and old guiding information in different nodes can originate firefighter  
Figure 11: _Cont._  
Figure 12: Costs of the new and old escape paths computed with the measurements at \(t_{2}\). guiding loops. Loss of local guiding information is solved by re-flooding updated packets. As said before, the Base Station keeps track of _SeqF_ the version of the local information stored in each node and triggers new flooding if the actual escape paths are unsuitable. After 10,000 simulations, not a cycle was created by the proposed system. For safety, a simple guiding loop detection method for mobile nodes was devised: if a cycle is detected the mobile node warns the firefighter.
## 4 Algorithm Design
### Resource Adjustment Within A Category
#### 4.4.2 Fine-Tuning of Bandwidth Allocation  
The more bandwidth an IoT device occupies, the greater its channel rate. Therefore, in the process of fine-tuning, bandwidth is reallocated from IoT devices with the highest  
Figure 4: Bandwidth allocation model.  
channel capacity to IoT devices with the lowest channel capacity. For \(N_{t}\) IoT devices, and \(t\in\{1,\dots,T\}\), the bandwidth fine-tuning can be described as follows:  
\[k_{t}^{Rmin^{\prime}} =k_{t}^{Rmin}+f_{t}^{bd} \tag{29}\] \[k_{t}^{Rmax^{\prime}} =k_{t}^{Rmax}-f_{t}^{bd}\] (30) \[f_{t}^{bd} =\text{random}(0,c_{4}\times r\times k_{t}^{cmax}) \tag{31}\]  
where \(k_{t}^{Rmin}\) and \(k_{t}^{Rmax}\) represent the proportion of bandwidth occupied by the IoT devices currently having the minimum and maximum channel rates in \(I_{t}\). \(f_{t}^{bd}\) indicates that the fine-tuning amount of bandwidth is also the transfer amount, which is randomly determined within \((0,c_{4}\times r\times k_{t}^{Rmax})\). \(c_{4}\) is a parameter between 0 and 1 and is defined as 0.5 here. Similarly, \(r\) as a decay rate parameter will be introduced later.
# Global GNSS-RO Electron Density in the Lower Ionosphere
## Appendix B GNSS-RO Data Availability and Statistics  
The global GNSS-RO observations can be perhaps divided into three periods in terms of the total number of daily RO profiles (Figure 11): CHAMP-period (2001-2006), COSMIC1-period (2006-2019), and COSMIC2-commercial-period (2019-). Advanced satellite RO receiver technologies play a critical role in the increase of the number of RO acquisitions from space. The BlackJack RO receiver on CHAMP, developed by NASA Jet Propulsion Laboratory (JPL), was able to track a dual-frequency GPS signals for precise (cm accuracy range) orbit determination and continuous coverage [88]. In its occultation mode, the receiver software was also able to schedule every 50 Hz tracking of setting occultations of up to four GPS satellites. The BlackJack on CHAMP has only aft antennas for the RO sounding, which yielded \(\sim\)250 profiles per day. The sampling was further improved by tracking rising occultations with the open-loop (OL) tracking successfully demonstrated on the SAC-C and later COMSIC-1 satellites. This advance led to the dual-antenna (aft and fore) and OL tracking IGOR (Integrated GPS and Occultation Receiver) implemented by the COMSIC-1 constellation [89], which produced \(\sim\)700 daily ROs per satellite, or an average total of \(\sim\)4000 daily ROs from the six-satellite constellation. A recent boost in RO acquisitions comes from the combination of a new four-antenna TriG (tri-band GNSS) receiver [90] implemented by the COSMIC-2 constellation and commercial CubeSat constellations (e.g., Spire) [91]. The full diurnal coverage with GNSS-RO was not achieved on a monthly basis until the COSMIC-1 constellation was launched. Prior to COSMIC-1, CHAMP made a diurnal sampling through a single orbital precession from multi-month or seasonal composites. The lengthy averaging for ionospheric observations can create aliasing problems between diurnal and seasonal variations, whereas the averaging over the same month from different years may bring interannual variability to the diurnal characteristics. Hence, a dense spatiotemporal sampling on a monthly basis is needed and critical for characterizing ionospheric variability. While COSMIC-1 achieved the needed monthly diurnal coverage initially, the six-satellite constellation degraded after 2016 with fewer satellites in operation. As seen in Figure 12, large gaps appeared in the GNSS-RO LST sampling. This creates a challenge to use the COSMIC-1 data for studying solar-cycle variations of the ionosphere. As a result, the long-term monitoring of ionospheric variability is limited to the observations at a few LSTs such as the 9 AM and 21 AM at which the combined operation from MetOp-A/B/C has provided more than 14 years of RO data. Figure 17: Number of daily GNSS-RO profiles since the CHAMP mission. Recent rising numbers are mainly from the COSMIC-2 and Spire constellations. The Spire RO numbers are based on the data archived under NASA’s CSDA program.
# An Improved Adam's Algorithm for Stomach Image Classification
## 2 Design of the CG-Adam Algorithm
### Gradient Norm Joint Clipping  
Gradient norm joint clipping trims both the gradient and the norm to ensure the stability and reasonableness of the gradient during the optimization process. By limiting the gradient to a suitable range, the gradient explosion and gradient vanishing problems can be avoided, and it helps to accelerate the convergence of the optimization process. Therefore, the use of the gradient norm joint clipping technique can effectively improve the training effect of the optimization algorithm and increase the performance and reliability of the algorithm. In addition, this technique enhances the stability of training by maintaining the proper scale of the gradient during optimization and reducing unwanted fluctuations. It also allows for a more flexible setting of gradient thresholds and fine control of the optimizer's behavior, enabling the optimization algorithm to adaptively adjust at different training stages, improving overall training efficiency and model quality. With these improvements, gradient norm joint clipping provides a stronger foundation for optimization algorithms to effectively address complex training challenges and improve the performance of the final model. The mathematical expression for gradient norm joint clipping is shown in Equation (7):  
\[g_{t}^{\prime}=g_{t}/\max(1,\left\|g_{t}\right\|_{2}/clip\_value) \tag{7}\]  
In Equation (7), \(g_{t}\) is the original gradient of the model parameters at step \(t\) of the iteration. \(g_{t}^{\prime}\) is the clipped gradient. \(\left\|g_{t}\right\|_{2}\) is the L2 norm of the original gradient, which is the length of the gradient vector in Euclidean space. \(clip\_value\) is the default clipping threshold that specifies the maximum allowable length of the gradient vector. \(\max(1,\left\|g_{t}\right\|_{2}/clip\_value)\) calculates a clipping factor, which is the greater between 1 and the maximum value of the ratio of the gradient L2 norm to the \(clip\_value\) threshold. If \(\left\|g_{t}\right\|_{2}\) is less than or equal to the trimming threshold, the clipping factor is 1; otherwise, it is \(\left\|g_{t}\right\|_{2}/clip\_value\). \(g_{t}^{\prime}\) stands for cropping the gradient \(t\) by dividing \(g_{t}\) by the cropping factor. If the L2 norm of the gradient is less than or equal to the cropping threshold, the cropping factor is 1, and the gradient is unchanged; otherwise, the gradient is scaled.  
Gradient norm joint clipping can simultaneously limit the size of the gradient and the number of gradient paradigms, effectively controlling the magnitude of the gradient change, thus improving the stability of the optimization algorithm. By clipping the gradient and the number of gradient paradigms, the effects of gradient explosion, gradient vanishing, and outliers on the optimization process can be prevented, which helps the model converge faster and improves the model's generalization ability.
## 3 System Model  
### Network Model  
Assuming that N nodes are randomly dispersed within a Z*Z two-dimensional square, the CHs collect sensed data from all CMs (cluster members) through clusters, aggregate them, and then transmit data to the BS. The structure of WSN is depicted in Figure 1.  
The network model is formulated based on the following considerations:  
* The sensor nodes are homogeneous and static and are identified by their corresponding IDs;
* The distance between nodes is estimated based on the received signal strength indicator (RSSI);
* Wireless connections are bidirectional and symmetric;
* No energy constraint is attached to the BS;
* The sensor nodes communicate their information to the CHs and are in charge of collecting, aggregating, and sending the information to the BS directly or through other CHs.
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 5 Heuristic Algorithm for Energy-Aware Routing
### Pivot Selection  
Valiant routing can lead to calculating long, multihop transmission paths. Pivots are randomly selected; in the original algorithm, their selection does not depend on the distance to the source node. To minimize network delays and reduce additional traffic, pivots can be selected from the subsets \(N_{s}\) assigned to traffic sources \(s\in S\), where \(S\subset N\) denotes the set of flow-initiating servers. In our research, the algorithm of selecting pivots for flows generated by \(s\)-th server (\(s\in S\)) is as follows:  
* Set the accepted maximum distance \(h_{s}\) between the \(s\)-th source and pivots (in hops);
* Select all nodes with maximal distance to \(s\) less or equal to \(h_{s}\), and create a set \(N_{s}\subset N\) of potential candidates for pivot nodes.  
Such modification of the Valiant algorithm prevents transmitting data via far relay nodes and concentrates the traffic as close as possible to the sources of flows. A depth-first search algorithm described in [41] can be used to determine the subsets \(N_{s}\) for all \(s\in S\). In our research, the search depth was equal to \(h_{s}\).  
Next, pivots for each transmitting flow \(m\in M\) are selected. Algorithm 1 described in Section 5.1 was adopted to determine pivot nodes. Pivots are usually not directly connected to flow source servers. Due to multiple equal-cost paths between the examined node \(q\in N_{s}\), \(s\in S\), and the flow source node, it is impossible to precisely predict the number of the port that will handle a given flow and check its current load. Therefore, instead of the current load \(l_{ip}\) of a given port \(p\) of the examined node \(i\), the average load of all ports of \(i\) is used in Algorithm 1. Hence, the average loads for all currently enabled nodes from the set \(N_{s}\) are calculated and compared. The node with the lowest average port utilization is included in the set of pivots \(Q\).
## 5 Experimental Result  
### Experiment Design  
In the simulation experiments, surveillance targets and available positions of cameras were generated based on the real-world bridge data obtained from the Republic of Korea Ministry of Land, Infrastructure and Transport's 2015 national geographic information statistics. First, we extracted from the data the distribution of the width, height, and length of about 600 bridges. Then, based on the number of target grid points, we divided the bridges into three categories: small, medium, and large. We also generated 40 problem sets for each category, which were used to compare the average performance of the algorithms and identify the feasible camera locations based on the map data. For each problem set, we repeated the simulation experiments for three minimum coverage levels (80%, 85%, and 90%). Table 3 shows the experimental parameters for all the algorithms used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Values** \\ \hline \multirow{4}{*}{Visibility test} & Minimum covered target grid points & 90 \\  & Camera grid width (\(w_{s}\)) (meter) & 10 \\  & Target grid width (\(w_{o}\)) (meter) & 5 \\  & Maximum size of 3D space (\(x_{s},y_{s},z_{s}\)) (meter) & (1000, 220, 50) \\ \hline \multirow{4}{*}{GA} & Mutation rate & 0.1 \\  & Crossover rate & 0.8 \\ \cline{1-1}  & Tournament rate & 0.7 \\ \cline{1-1}  & Maximum number of cameras & 200 \\ \cline{1-1}  & Number of generations & 200 \\ \cline{1-1}  & Number of population & 500 \\ \hline ULA & Uniqueness weight (\(\alpha\)) & \(1\) \\ \hline \multirow{2}{*}{Distribution of bridges} & Length (meter) & N(688, 652) \\  & Width (meter) & N(14, 52) \\ \cline{1-1}  & Height (meter) & N(21.3, 52) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameters. GA: genetic algorithm; ULA: local search algorithm.  
All the algorithms were coded in C# and were run on a PC with an Intel _i5_ 3.4 GHz processor and 8 GB RAM. In order to evaluate the performance of a probabilistic search method, several pilot experimental tests were conducted to select the best parameter values for GA and we repeated the simulation experiments for different surveillance target sizes.