esa-sceva/satcom-chunk-collection huggingface dataset:
# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
## 2 Materials and Methods
### Yield Data  
Since this research was conducted on operational crop fields, actual yield data for each wheat field were provided by the farmer. The data were collected using yield mapping equipment, specifically, the FarmTRX(tm) system mounted on combine machinery. The data were presented in vector point format (ESRI shapefile) and underwent pre-processing to remove any inaccurate or erroneous value entries, including extreme high and low yield values.  
The growing season for this dataset extends from December 2023 to June 2024. Descriptive statistics for each wheat field are summarized in Table 5, while the spatial distribution, and interpolated yield value derived from the Inverse Distance Weighted method (IDW), of yield in each wheat crop field is illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phenological Stage** & **Days per Stage** & **BBCH Scale** & **Imagery Acquisition Date** \\ \hline Emergence & 7–14 days & BBCH 10–19 & 13 January 2024 \\ \hline Tillering & 25–40 days & BBCH 20–29 & 26 January 2024 \\  & & BBCH 20–29 & 26 January 2024 \\  & & BBCH 20–29 & 15 February 2024 \\  & & BBCH 20–29 & 17 February 2024 \\ \hline Stem Elongation & 20–30 days & BBCH 30–39 & 3 March 2024 \\  & & BBCH 30–49 & 21 March 2024 \\ \hline Booting & 20–30 days & BBCH 40–49 & 28 March 2024 \\ \hline Heading & 7–14 days & BBCH 51–59 & 15 April 2024 \\ \hline Flowering & 5–10 days & BBCH 61–69 & 7 May 2024 \\ \hline Grain Filling & 10–20 days & BBCH 71–77 & 22 May 2024 \\ \hline Maturity and Harvest & 10–20 days & BBCH 83–99 & 9 June 2024 \\ \hline Post Harvest & & & 24 June 2024 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Wheat phenological stages and the corresponding imagery acquisition date.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Field ID** & **Mean** & **Standard** & **Minimum** & **Maximum** & **Range** \\
**(Kg/stremma *)** & **Deviation** & **(Kg/stremma *)** & **(Kg/stremma *)** & **(Kg/stremma *)** \\ \hline \multicolumn{5}{c}{Soft Wheat} \\ \hline FID 1 & 373.54 & 127.00 & 100.28 & 737.88 & 637.60 \\ FID 2 & 598.44 & 94.25 & 351.06 & 841.57 & 490.51 \\ FID 3 & 635.82 & 86.61 & 350.37 & 898.26 & 547.89 \\ FID 4 & 605.32 & 115.08 & 150.12 & 649.91 & 499.79 \\ \hline \multicolumn{5}{c}{Durum Wheat} \\ \hline FID 1 & 425.59 & 105.98 & 150.12 & 649.91 & 499.79 \\ FID 2 & 290.09 & 73.16 & 119.88 & 534.89 & 415.01 \\ FID 3 & 258.73 & 82.42 & 61.04 & 542.13 & 481.09 \\ FID 4 & 367.79 & 86.24 & 200.47 & 645.8 & 445.33 \\ FID 6 & 339.34 & 63.99 & 200.6 & 561.36 & 360.76 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Descriptive statistics of the measured yield production per operational field and crop.
## 5 Performance Evaluation
### Experimental Study
#### 5.2.2 Impact of Traffic Load  
The purpose of this scenario is to test the behavior of the network when the nodes have more inbound traffic, more child nodes, or an overlapping schedule. We use the network layout of Figure 1. Three configurations for the data rate are used for latency and duty cycle, namely, 1, 2, and 6 pkts/min. Figure 8a shows that the latency performance is degraded for Orchestra as the traffic load increases. This is mainly due to the lack of transmission opportunities of nodes closer to the root. OSCAR reacts better to dense traffic and thus exhibits better latency overall by allocating more time slots as necessary. This is particularly notable in the 6 pkts/min scenario where the average latency is hal  
Figure 7: Average packet delivery ratio of OSCAR and Orchestra: (**a**) first topology and (**b**) second topology.  
The previous performance gains are achieved, as can be seen in Figure 8b, by slightly sacrificing power consumption in the highest traffic load scenario, as OSCAR grants more cells to the congested nodes. However, this increased consumption occurs only when the network is overloaded. We notice that OSCAR does not significantly increase the average duty cycle compared to the improvements achieved in latency. Finally, the average rate of packet delivery between OSCAR and Orchestra as a function of the data rate is plotted in Figure 8c. In this experiment, each node sent 2000 packets to the root, for a total of 20,000 packets sent across the network. In addition, a packet transmission intervals of 12 s, 6 s, and 5 s are used. It can be seen that Orchestra's packet delivery performance is degraded as the traffic load increases. With a data rate of 5 pkts/min, OSCAR shows a result of 96.1% against 87.5% for Orchestra. When the packet transmission interval is increased to 15 pkts/min, Orchestra's packet delivery ratio worsens considerably and is reduced to 76.4% compared to 90.7% for OSCAR. Better reliability is exhibited by OSCAR mainly due to the fact that it allots more unicast slots as the packets approach the sink.
# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 5 Explainability in Federated Intrusion Detection
### Techniques for Explainable FL  
To ensure transparency and trust in FL-based intrusion detection systems, it is crucial to use explainable AI (XAI) methods. In this context, post hoc and model-intrinsic techniques have been adapted to accommodate the distributed and privacy-sensitive nature of FL.  
Among the post hoc methods, SHAP (SHapley Additive exPlanations) is notable for assigning consistent and locally accurate feature importance scores across heterogeneous clients. Its use in FL has been validated through methods such as Federated SHAP, in which each client computes local Shapley values and transmits masked contributions to the aggregator. This process preserves privacy while enabling global interpretability [40; 42].  
Another widely used method is LIME (Local Interpretable Model-Agnostic Explanations), which builds interpretable local surrogate models. In FL settings, federated LIME enables clients to generate local explanations based on perturbed data samples, helping analysts comprehend individual anomaly classifications without disclosing raw data [2; 43].  
Grad-CAM (Gradient-Weighted Class Activation Mapping), originally designed for convolutional neural networks in image classification, has been adapted to visualize important input regions in CNN classifiers used for intrusion detection in traffic [44].  
In addition, attention-based mechanisms and layer-wise relevance propagation (LRP) have shown promise in capturing feature relevance across layers in deep federated models (see refs. [45; 46]). These methods support client-side and global interpretability while maintaining compliance with privacy constraints.  
In FL-based IDS scenarios, these explainability techniques can be employed at two levels:  
* Client level: To support real-time interpretation of alerts and assist in localized forensic analysis;
* Aggregator level: They produce global attribution maps that identify common threat vectors across clients without accessing raw features.  
Integrating explainable methods into FL pipelines enables cybersecurity analysts to enhance transparency and response quality in distributed intrusion detection systems.
## 4 Security and Performance Analyses
### Authentication Proof of the Proposed Scheme Using BAN Logic
#### 4.1.5. Verification  
By using Message \(\mathbf{M_{2}}\),  
\[ \begin{array}{c}MCS\triangleleft\{NID_{D},NID_{P},V_{1}:h(X_{P}\|P_{P}\|Cer_ {P}\|R_{P}),\\ V_{3}:h(ID_{D}\|Cer_{D}\|PHI\|R_{D}),C_{P},D_{PHI}:PHI_{Cer_{D}},T_{a}(x),T_{b} (x),TS_{1},TS_{2}\}.\end{array}  \tag{5}\]  
From Rule 1 and \(AS_{5}\),  
\[S_{1}\text{: }MCS\mid\equiv Pat\mid\sim\ R_{P}. \tag{6}\]  
From Rule 2 and \(AS_{1}\),  
\[S_{2}\text{: }MCS\mid\equiv Pat\mid\equiv R_{P}. \tag{7}\]  
From Rule 3 and \(AS_{7}\),  
\[S_{3}\text{: }MCS\mid\equiv R_{P}. \tag{8}\]  
From Rule 4, \(AS_{1}\) and \(S_{2}\),  
\[S_{4}\text{: }MCS\mid\equiv MCS\stackrel{{ SK}}{{\leftrightarrow}}Pat.\]Further, using Rule 2, \(\mathbf{AS_{1}}\) and \(S_{1}\),  
\[S_{5}\text{: }\ MCS\ \big{|}\equiv\text{\it Pat}\ \big{|}\equiv\text{\it MCS}\ \overset{\text{\it SR}}{\leftrightarrow}\text{\it Pat}.\ \textbf{Goal 1} \tag{9}\]  
By using similar arguments, from Rule 1 and \(\mathbf{AS_{6}}\),  
\[S_{6}\text{: }\ MCS\ |\equiv\text{\it Doc}\ |\sim R_{D}. \tag{10}\]  
From Rule 2 and \(\mathbf{AS_{2}}\) and \(S_{6}\),  
\[S_{7}\text{: }\ MCS\ |\equiv\text{\it Doc}\ |\equiv R_{D}. \tag{11}\]  
From Rule 3 and \(\mathbf{AS_{8}}\),  
\[S_{8}\text{: }\ MCS\ |\equiv R_{D}. \tag{12}\]  
According to Rule 4, \(\mathbf{AS_{2}}\) and \(S_{7}\),  
\[S_{9}\text{: }\ MCS\ \big{|}\equiv\text{\it MCS}\ \overset{\text{\it SR}}{ \leftrightarrow}\text{\it Doc}. \tag{13}\]  
Using Rule 2, \(\mathbf{AS_{2}}\) and \(S_{6}\), we have  
\[S_{10}\text{: }\ MCS\ \big{|}\equiv\text{\it Doc}\ \big{|}\equiv\text{\it MCS} \ \overset{\text{\it SR}}{\leftrightarrow}\text{\it Doc}.\ \textbf{Goal 2}\]  
By using Message \(\mathbf{M_{3}}\),  
\[ \begin{array}{c}\text{\it Doc}\triangleleft\{\text{\it QID}_{P}:\langle NID _{P}^{new}\|X_{P}^{new}\rangle_{SK_{\text{MCSP}}},V_{4}:h(ID_{D}\|R_{D}),\\ V_{5}:h\big{(}NID_{P}^{new}\|ID_{P}\|SK_{\text{MCSP}}\|R_{P}\|X_{P}^{new} \rangle,T_{c}(x)\big{\}}\end{array}
## 3 Results
### System Distributability
#### 3.3.2 Swarm Performance  
Timing and Accuracy  
Figure 10 shows the average time the swarm phase takes when allowing agents to move freely between available nodes. Using this method, the swarming phase operates  
Figure 8: Interaction time (ms) for 1250 agents on 1, 4, 9, and 16 nodes, using data from Section 2.2.1 with cellular morphology features.  
Figure 9: Interaction time (ms) for 5000 agents on 1, 4, 9, and 16 nodes, using data from Section 2.2.1 with cellular morphology features.  
exactly as it would when a single node is used; however, it is distributed across multiple nodes, with agents frequently moving between nodes. Runtime initially decreases with two nodes, dropping from 9128 ms to 6788 ms, and continues to drop to 5421 ms with four nodes and 4918 ms with nine nodes. However, runtime increases with 16 nodes, to 5301 ms. The swarming phase is not compute-intensive, but when free movement is allowed between nodes it is very network transfer-intensive, which is demonstrated with the increase in runtime when moving from 9 nodes to 16 nodes.  
Figure 11 shows the average runtime for the swarm phase when disallowing agent movement between nodes. Agents must remain on the node they are initialized on, all swarming processes happen locally on each node, with each node represented in a meta-swarm phase once localized swarming is complete. Using this method we see runtime continue to decrease with additional nodes, indicating the increase in runtime using the previous method was from network transfers between nodes.  
Figure 12 shows the prediction accuracy using both of the above methods. In all cases, the predictive performance is decreased when swarming is node-local. The accuracy drops minimally in two of the three examples; dropping from 82.95% to 82.51%, a decrease of  
Figure 11: Swarm timing (ms) for 1250 agents on 1, 2, 4, 9, and 16 nodes, using data from Section 2.2.1 with cellular morphology features, with node-localized swarming.  
Figure 10: Swarm timing (ms) for 1250 agents on 1, 2, 4, 9, and 16 nodes, using data from Section 2.2.1 with cellular morphology features, with agents moving freely between nodes.  
0.44% in the breast cancer dataset and from 95.30% to 94.27%, a decrease of 1.03%, in the airline passenger satisfaction dataset. However, the accuracy drops substantially when predicting Hollywood movie success, going from 81.0% to 74.97%, a decrease of 6.03%. It is currently unclear why this dataset has a larger decrease in predictive performance using the node-local swarming method, a phenomenon that can be studied in future work.
## 3 Experiments and Results
### Inclination Calibration
To ensure an accurate comparison, we also performed mass estimation under static conditions across various postures with different orientations, as shown in Table 8. The overall trends are consistent with the previous experiment: crosstalk compen \begin{table}
\begin{tabular}{l c c c c} \hline  & **MSE** & **Mean** & **MAE** & **Variance** \\ \hline _No Calibration_ & 0.180 & 1.730 & 0.302 & 0.107 \\ _LSM Calibration_ & 0.109 & 2.308 & 0.308 & 0.014 \\ _Full Calibration_ & 0.009 & 1.993 & 0.081 & 0.009 \\ Improved (\%) & 95.0 & 97.7 & 73.2 & 92.6 \\ \hline \end{tabular}
\end{table} 
Table 7: The table of mass estimation results [kg] for the three different calibration scenarios. Figure 9: The graph showing the mass estimation results for the three different calibration scenarios. sation enabled force and torque separation, while incline compensation allowed for more accurate estimations. Additionally, we evaluate whether the external force converges to 0 when no object is grasped and to 2 kg when a 2 kg object is grasped by repeatedly performing the same pick-and-place scenario under various base inclination angles, as shown in Table 9. The base inclinations are set to roll angles of \(-7^{\circ}\) and \(-15^{\circ}\) and pitch angles of \(-8^{\circ}\) and \(-17^{\circ}\). The grippers used are the Robotiq 3f and Barrett hand. Similar to previous experimental results, the _No Calibration_ scenario exhibits significant errors. While the _LSM Calibration_ reduces errors to some extent, substantial errors still remain. With _Full Calibration_, the errors are significantly reduced compared to both _No Calibration_ and _LSM Calibration_, achieving a maximum reduction of 98.7%. Consistent with our experimental analysis, these results indicate that compensating for inclination plays the most significant role in accurately estimating a consistent mass.
## 5 De-Noising Filter Method and Experimental Results
### De-Noising Filter Model
After, we use the \"best LSTM model\" previously validated in the \"validation part\", to predict the standardization ENU errors. Then, we unstandardize them to obtain the predicted ENU errors. Finally, we obtain the estimated positions by rEKF-LSTM, which are computed as the difference between the estimated positions by robust-EKF and the predicted errors following Equation (83). The estimated positions by rEKF-LSTM are written as follows:  
\[i_{k}=\sigma(W_{i}x_{k}+R_{i}h_{k-1}+b_{i}) \tag{78}\]0
## 2 Materials and Methods
### Nonlinear Parameters Calculation
#### 2.2.2 Fractal Dimension (FD)  
The FD was calculated using [PERSON]'s algorithm [38]. [PERSON]'s algorithm calculates the FD directly from the time series. Reconstruction of the attractor phase space is not necessary; therefore, this algorithm is simpler and faster than other classical measures derived from chaos theory. Moreover, it can be applied to short time series [38]. [PERSON]'s algorithm can be described as follows:  
1. For one dimensional time series: \(\mathbf{X}=\mathbf{x}[\mathbf{1}],\mathbf{x}[\mathbf{2}],\ldots,\mathbf{x}[\mathbf{N}]\), a new \(\mathbf{k}\) time series can be formed as follows: \[\mathbf{X}_{k}^{m}=\mathbf{x}[\mathbf{m}],\mathbf{x}[\mathbf{m}+\mathbf{k}],\mathbf{x}[\mathbf{m}+\mathbf{2}\mathbf{k} ],\ldots,\mathbf{x}\bigg{[}\mathbf{m}+\mathbf{int}\bigg{(}\frac{\mathbf{N}-\mathbf{m}}{\mathbf{k}} \bigg{)}\cdot\mathbf{k}\bigg{]},\] where \(\mathbf{k}\) and \(\mathbf{m}\) are integers, \(\mathbf{int}\big{(}\frac{\mathbf{N}-\mathbf{m}}{\mathbf{k}}\big{)}\) is the integral part of \(\big{(}\frac{\mathbf{N}-\mathbf{m}}{\mathbf{k}}\big{)}\), \(\mathbf{k}\) indicates the discrete time interval between points, whereas \(\mathbf{m}\) = \(\mathbf{1}\), \(\mathbf{2}\),..., \(\mathbf{k}\).
2. The length of each new time series can be defined as follows: \[\mathbf{L}(\mathbf{m},\mathbf{k})=\bigg{(}\sum_{i=1}^{\mathbf{int}\big{(}\frac{\mathbf{N}-\mathbf{m}} {\mathbf{k}}\big{)}|\mathbf{x}[\mathbf{m}+\mathbf{i}\mathbf{k}]-\mathbf{x}[\mathbf{m}+(\mathbf{i}-\mathbf{1})\mathbf{k} ]|\bigg{)}\cdot\bigg{(}\frac{\mathbf{N}-\mathbf{1}}{\mathbf{int}\big{(}\frac{\mathbf{N}-\mathbf{m} }{\mathbf{k}}\big{)}\mathbf{k}^{2}}\bigg{)},\] where \(\mathbf{N}\) is length of the original time series \(\mathbf{X}\).
3. The length of the curve for the time interval \(\mathbf{k}\) is defined as the average of the \(\mathbf{k}\) values \(\mathbf{L}(\mathbf{m},\mathbf{k})\), for \(\mathbf{m}\) = \(\mathbf{1}\), \(\mathbf{2}\),..., \(\mathbf{k}\): \[\mathbf{L}(\mathbf{k})=\frac{\mathbf{1}}{\mathbf{k}}\sum_{m=1}^{\mathbf{k}}\mathbf{L}(\mathbf{m},\mathbf{k}).\] Finally, when \(\mathbf{L}(\mathbf{k})\) is plotted against \(\mathbf{1}/\mathbf{k}\) on a double logarithmic scale, with \(\mathbf{k}\) = \(\mathbf{1}\), \(\mathbf{2}\),..., \(\mathbf{k}_{max}\), the data should fall on a straight line, with a slope equal to the FD of \(\mathbf{X}\). Thus, [PERSON]'s FD is defined as the slope of the line that fits the pairs \(\big{(}\mathbf{ln}\big{[}\mathbf{L}(\mathbf{k}),\ \mathbf{ln}\big{(}\frac{\mathbf{1}}{\mathbf{k}} \big{)}\big{]}\big{)}\) in a least-squares sense. In order to choose an appropriate value of the parameter \(\mathbf{k}_{max}\), Higuchi's FD values were plotted against a range of \(\mathbf{k}_{max}\). The point at which the FD plateaus was considered a saturation point, and that \(\mathbf{k}_{max}\) value should be selected [39]. A value of \(\mathbf{k}_{max}\) = \(\mathbf{100}\) was chosen for our study.
## 5 Binary Tree Structure Compression
#### Context Mixing Method
Then, the arithmetic code can be computed efficiently so that it starts with the interval \([0,1)\), and, for each bit \(x_{i}\), the interval is divided into two parts proportional to \(p_{0}\) and \(p_{1}\) from Equation (15) and replaced by a sub-interval corresponding to \(p_{xi}\). If the range is \([low,high)\) and the probability that \(x_{i}\) is \(0\); then, the interval updates as follows:  
\[ \begin{split} mid=low+p_{0}(high-low)\\ [low,high)\leftarrow[low,mid)\ \ if\ x_{i}=0\\ [mid,high)\ \ if\ x_{i}=1\end{split}  \tag{17}\]  
As the interval shrinks, the leading digits of the \(low\) and \(high\) will match; then, these digits are outputted immediately. When the interval shrinks, the leading digits of \(low\) and \(high\) coincide and these digits are immediately output. The context shuffling method applied to binary tree compression consists of \(18\) context models (\(10\) models for general use, \(4\) models for long contexts, and \(4\) models for short contexts) and \(8\) sets of weights that choose a \(4\)-bit context consisting of the \(4\) most significant bits of the previous data stream. This method was implemented in an _EEDC-RANGE (BWT)_ encoder and was tested for the _horse_ image with a compression ratio of \(0.2\) bpp. The best result for the _AAE_ value is \(3.61\) using _EEDC-RANGE (BWT)_. When context mixing is used, the value of the _AAE_ parameter is \(3.47\), which is significantly better than the previous algorithm. The new algorithm is named _EEDC-BSSP_ (Block-Sorting Symbol Prediction) because it uses the block-sorting algorithm (Burrows-Wheeler Transform) for compressing the grey values and the symbol prediction method (Context Mixing Method) for compressing the binary tree structure.
# Digital Twins' Application for Geotechnical Engineering:
## 4 Future Research Perspectives for Geotechnical Digital Twins
Current applications predominantly reside at the basic descriptive and general diagnostic levels, collectively accounting for 71% of implementations [62]. Intelligent decision-making applications remain relatively scarce, while autonomous control applications are minimal. Geotechnical digital twins currently concentrate on the first two levels, with significant progress required to achieve advanced capabilities. Furthermore, lifecycle optimization of geotechnical digital twins warrants extensive exploration to enable comprehensive lifecycle management and extend their temporal application spans. Regarding spatial expansion, applications primarily encompass two categories: same-scale twin object coordination, and cross-scale twin object coordination. In geotechnical contexts, research on group tunnel excavation effects exemplifies same-scale twin challenges, while optimization of excavation-support-monitoring systems represents cross-scale coordination problems, both requiring in-depth investigation.
###### Abstract  
Land surface/ecosystem models (LSEMs) play a key role in understanding the Earth's climate. They represent ecosystem dynamics by simulating fluxes occurring between the biosphere and atmosphere. However, for a correct flux simulation, it is critical to calibrate the model using robust and state-of-the-art calibration techniques. In this work, we optimize parameters of the Integrated Model of Land Surface Processes (INLAND) using the hierarchical multi-objective calibration method (AMALGAM) to improve the representation of surface processes in a natural ecosystem over the Pampa biome in South America. The calibration was performed using experimental data of energy and CO\({}_{2}\) flux collected in a native field located in southern Brazil. We compared simulations using the default and calibrated parameter set. The results show that the calibration of the model significantly improved all fluxes analyzed. The mean errors and bias values were significantly reduced, and the seasonality of fluxes was better represented. This work is one of the first to apply a multi-objective calibration in an LSEM to represent surface fluxes in the Pampa biome, presenting a consistent set of parameters for future applications used in studies of biome land use and land cover.  
Keywords:land surface and ecosystem models; calibration; latent and sensitive heat fluxes; net ecosystem exchange; Pampa biome; Eddy covariance +
Footnote †: journal: _Geosciences_