esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### CPU Utilization  
Figure 12 shows the average CPU utilization during encryption for AES (Original) and our proposed scheme with 4, 6, and 8 nodes. The \(x\)-axis represents runtime (seconds), and the \(y\)-axis shows CPU utilization in percentage (%). As shown in the figure, the AES (Original) exhibits higher variation and sustained CPU activity over a longer runtime. The execution completes in approximately 440 s, with CPU utilization ranging from 5.00% to 75.76%. This indicates uneven processing and frequent spikes due to the sequential workload on a single node. In contrast, our proposed scheme shows lower and more stable CPU usage with shorter runtimes across all configurations. Node 4 completes in 314 s with utilization ranging from 13.84% to 62.10%, Node 6 in 140 s with 26.73% to 59.54%, and Node 8 in 85 s with 20.80% to 63.46%. Compared to AES (Original), the CPU utilization patterns of our proposed scheme demonstrate improved load balancing and lower processing overhead across nodes.  
These results demonstrate the advantages of our proposed scheme in terms of scalability and resource efficiency. Unlike AES (Original), which concentrates the entire encryption workload on a single node, our design distributes subset-encryption tasks across multiple nodes, enabling concurrent processing and reducing overall execution time. Despite utilizing more CPUs, our scheme maintains moderate and balanced usage across nodes,  
\begin{table}
\begin{tabular}{l l l l} \hline
**Workload** & **Vanilla** & **AES (Original)** & **Proposed (PQC + QKD + AES)** \\ \hline workload A & 255.65 & 291.43 & 267.74 \\ \hline workload B & 168.86 & 209.65 & 182.34 \\ \hline workload C & 115.55 & 173.61 & 153.32 \\ \hline workload D & 111.06 & 146.62 & 132.17 \\ \hline \end{tabular}
\end{table}
Table 2: Runtime of Vanilla, AES (Original), and Proposed (PQC + QKD + AES).  
avoiding excessive load on any single core. This reduces bottlenecks and ensures that increasing the number of nodes leads to faster runtime without causing resource saturation. In addition, although CPU utilization remains moderate rather than saturated, such behavior does not indicate idle time or resource under-utilization. Rather, the reduced utilization reflects effective parallelism and reduced workload per node (e.g., in an 8-node processing a 100 GB file, each node encrypts only approximately 12.5 GB independently as a subset). The consistent runtime improvements across 4, 6, and 8 nodes confirm that performance scales with added resources without incurring communication bottlenecks or wait-time stalls.
#### Sensor Protocols for Information via Negotiation (SPIN)  
SPIN is developed at MIT [7, 12]. It is a protocol designed for wireless sensor networks and has several similarities with directed diffusion. SPIN names its data using high-level data descriptors, the _meta-data_, compared to the _interest attributes_ used in directed diffusion. The meta-data and the raw data have a one-to-one mapping relation. The format of meta-data is application-specific. SPIN uses meta-data negotiations to eliminate redundant data transmissions over the network. Its two key innovations are _negotiation_ and _resource-adaptation_.  
In the SPIN protocol, the initiating node which has new data _advertises_ the data to the neighboring nodes in the network using meta-data. When the neighboring node wants this kind of data, it sends a _request_ to the initiator node for the data. The initiator node responds and _sends_ data to the sinks. Therefore, SPIN is essentially a _sender-initiated_ routing protocol.  
Each node has its own resource manager to keep track of the usage of energy resource. Before data transmission, each node polls its resources to make a decision whether it should participate in the activity or cut it back. SPIN is essentially a flooding protocol, however, the use of meta-data for negotiation and the adaptation to resource available on the sensor nodes help it eliminate most of the redundant data transfer, making it more prudent in forwarding third-party data.
# Wavelength Selection for Satellite Quantum Key Distribution
## 3 Channel Model  
To determine which wavelengths are feasible for QKD, we first characterise the key loss factors present in the communication channel. We focus on a satellite downlink channel where the main sources of degradation are diffraction, absorption, scattering, excess noise and turbulence-induced wavefront errors. Losses due to absorption, scattering and radiance are determined using MODTRAN. The total channel efficiency is calculated using Equation (1).  
\[\eta=\eta_{d}\ \eta_{ext}\ \eta_{FS}\ \eta_{opt}\ \eta_{det} \tag{1}\]  
where \(\eta_{d}\) is the geometric loss due to diffraction, \(\eta_{ext}\) is the atmospheric extinction losses resulting from absorption and scattering, \(\eta_{FS}\) is the field stop loss, \(\eta_{det}\) is the detector efficiency, and \(\eta_{opt}\) is the overall efficiency of the optical setup at the receiver including filter efficiencies. In the following sections, we briefly describe each of the losses. Positioning and tracking losses were not accounted for in this model. These are important when considering different telescope apertures, which will result in different field of views and spot sizes. They will be included in a future work.
# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 4 Experimental Evaluation
### Evaluation of the Remote Sensing Image Retrieval Algorithm  
#### 4.3.1 Evaluation Indicators  
_Precision@K_ and _Recall@K_ were used as evaluation metrics to evaluate the performance of the retrieval algorithms:  
1. _Precision@K: Precision@K_ measured the proportion of relevant instances among the top \(K\) retrieved items. It was calculated as the ratio of relevant retrieved instances to the total number of retrieved instances at rank \(K\). In this context, _Precision@K_ at \(K\) = 1, 5, and 10 evaluated the precision of the retrieval algorithm when the top 1, 5, and 10 retrieved items were considered, respectively. The equation for the calculation of _Precision@K_ is shown in Equation (4): \[Precision@K=\frac{TP@K}{K}\] (4) Among them, \(TP@K\) refers to the number of positive samples in the top \(K\) ranked retrieval results.
2. _Recall@K: Recall@K_ measured the proportion of relevant instances which were retrieved among all relevant instances. It was calculated as the ratio of relevant instances retrieved to the total number of relevant instances in the dataset at rank \(K\). Specifically, _Recall@K_ at \(K\) = 1, 5, and 10 assessed how well the retrieval algorithm captured relevant instances within the top 1, 5, and 10 retrieved items, respectively. The equation for the calculation of _Recall@K_ is shown in Equation (5): \[Recall@K=\frac{TP@K}{R}\] (5) Among them, \(TP@K\) refers to the number of positive samples within the top \(K\) ranked retrieval results, while \(R\) represents the total number of positive samples.
## 1 Introduction
Due to the fact that the prediction is conducted solely based on measurement noise in the form of false-positive detections in direct proximity to the sensor aperture, the models can directly be employed in an infrastructure and potentially also vehicle application without inducing concept drift. With this in mind, this work contributes to earlier research (see Section 2) as follows:  
1. Fine-granular weather classification by 15 standardized precipitation classes, corresponding to the WMO Surface SYNOP standard;
2. Intensity quantification by additional precipitation rate prediction;
3. Generalizability with respect to the background scene by noise extraction;
4. Implementation based on a series-deployed automotive solid-state LiDAR;
5. Benchmark of state-of-the-art deep learning models for image time series processing. Since, the underlying dataset was recorded in a static infrastructure measurement unit, however, the effect of varying driving speed and corresponding headwind on the noise characteristics will need to be investigated in future works. Also, secondary effects in dynamic driving scenarios, like spray water, might have a major impact on the prediction performance. The work is structured as follows. Following the introduction, the second section elaborates on related works regarding LiDAR- and machine learning (ML)-based weather characterization for automotive or infrastructure applications. The third section describes the weather-induced measurement effects in LiDAR sensors which are exploited to predict the occurring weather conditions. In the next section, the in-field measurement setup for automated data acquisition and the resulting dataset are introduced. The fifth section lists and explains the architecture as well as the corresponding training and hyperparameter optimization methodology of the evaluated deep learning (DL) models. In the subsequent sixth section, the corresponding results are presented and the model performances are compared quantitatively with respect to classification accuracy and regression error, as well as model size and required computational resources. The last section concludes the work by summarizing the methodology and results.
# Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches
## 3 Results and Discussion
### Data Reduction  
To incorporate HSI technology in the industrial almond processing lines, it is important to select the most relevant wavelengths as it reduces the computational time. Therefore, the wavelengths of the greatest importance for classifying almonds according to their bitterness were selected using the VIP scores obtained from the global model (Table 1). Figure 4 shows the graphical representation of the VIP scores together with the most relevant wavelengths.  
As can be seen in the figure, the most relevant wavelengths for the discrimination between sweet and bitter almonds are at around 1112-1161, 1191, 1395 and 1428 nm.  
Table 2 shows the cross-validation results for the best classification model obtained using the wavelength range selected.  
The new classification models showed similar results to those obtained using the complete spectral range, with a classification success of 99 and 100% for the categories sweet and bitter, respectively. This is of great interest because although the number of wavelengths has been reduced by 86% (from 179 bands to 25), the classification capability of the model was not affected.  
Next, pixel-by-pixel validation of the mixtures was carried out to test the robustness of the model [_Data not shown_]. In this case, the new model obtained did not enable the correct identification of the bitter almonds present in the mixtures, indicating that the selection of wavelengths is not feasible for this specific objective. It is important to note that each spectrum is a detailed profile or unique fingerprint, so reducing the number of bands could eliminate essential information.
## 2 Systems Description  
### Converter Model  
The power circuit of the three phase two-level voltage source converter (2L-VSC), is considered, where the two switches in each leg operate in a complementary mode. The switching states can be represented by the switching signals \(S_{ka}\), \(S_{kb}\), and \(S_{kc}\), which are defined as follows:  
\[S_{ka}=\begin{cases}1\text{ if }S_{1}\text{ on and }S_{4}\text{ off}\\ 0\text{ if }S_{1}\text{ off and }S_{4}\text{ on}\end{cases} \tag{1}\]  
\[S_{kb}=\begin{cases}1\text{ if }S_{2}\text{ on and }S_{5}\text{ off}\\ 0\text{ if }S_{2}\text{ off and }S_{5}\text{ on}\end{cases} \tag{2}\]  
\[S_{kc}=\begin{cases}1\text{ if }S_{3}\text{ on and }S_{6}\text{ off}\\ 0\text{ if }S_{3}\text{ off and }S_{6}\text{ on}\end{cases} \tag{3}\]  
Figure 1: Power circuit schematic for a stand-alone voltage source converter (VSC).  
The filter inductance (\(L_{f_{a^{\prime}}}\), \(L_{f_{b^{\prime}}}\), \(L_{f_{c}}\)) equation can be expressed in the vectorial form as:  
\[L_{f}\frac{\mathrm{d}i_{L_{f}}}{\mathrm{d}t}=V_{i}-V_{c_{f}} \tag{4}\]  
where \(L_{f}\) is the filter inductance. The equation that describes the dynamic behaviour of the output voltage can be expressed mathematically as:  
\[C_{f}\frac{\mathrm{d}V_{c_{f}}}{\mathrm{d}t}=i_{L_{f}}-i_{o} \tag{5}\]  
where \(C_{f}\) is the filter capacitance (\(C_{f_{a^{\prime}}}C_{f_{b^{\prime}}}\), \(C_{f_{c}}\)). These equations can be rewritten in the state space model as:  
\[\frac{\mathrm{d}X}{\mathrm{d}t}=AX+B_{1}V_{i}+B_{2}i_{o} \tag{6}\]  
where,  
\[X=\begin{bmatrix}i_{L_{f}}\\ V_{c_{f}}\end{bmatrix} \tag{7}\]  
\[A=\begin{bmatrix}-R/L_{f}&-1/L_{f}\\ 1/C_{f}&0\end{bmatrix} \tag{8}\]  
\[B_{1}=\begin{bmatrix}1/L_{f}\\ 0\end{bmatrix} \tag{9}\]  
\[B_{2}=\begin{bmatrix}0\\ -1/C_{f}\end{bmatrix} \tag{10}\]  
where \(i_{L_{f}}\) and \(V_{c_{f}}\) are the filter current and voltage respectively. \(i_{o}\) is the load current, which can be estimated or measured. \(V_{i}\) is the inverter voltage of the system and it has eight different voltage vectors as shown in Figure 2.  
A discrete model is obtained from (6) and it can be expressed as follows:  
\[x\left(k+1\right)=A_{q}x\left(k\right)+B_{q}V_{i}\left(k\right)+B_{dq}i_{o} \left(k\right) \tag{11}\]  
where,  
\[A_{q}=exp^{AT_{q}} \tag{12}\]  
Figure 2: Voltage vectors generated by the inverter (\(V_{i}\)).  
\[B_{q}=\int_{0}^{T_{s}}exp^{A\tau}B_{1}d\tau \tag{13}\]  
\[B_{dq}=\int_{0}^{T_{s}}exp^{A\tau}B_{2}d\tau. \tag{14}\]  
This model is used to calculate the prediction of filter voltages and currents for every possible input voltage. The selection of the optimal input voltage depends on the evaluation of CF [18]. Consequently, the new switching states are applied to the converter for the next sampling time.
## 4 Experiment Study  
### Numerical Analysis of Noise Covariance Estimation  
A numerical simulation of noise covariance estimation for the LTI system with the LTI measurement model in (16) is provided to show the performance of the ALS algorithm, where the sampling time, the state, and measurement transitional matrices are set as \(T_{s}=1\), \(F=\left[\begin{array}{cc}0.7T_{s}&0.3\\ -0.1&0.9T_{s}\end{array}\right]\) and \(H=[0.5,0.2]\).  
In this simulation study, the ALS algorithm was operated with \(N_{\text{MC}}=500\) Monte Carlo simulations over \(N_{time}=150\) time steps. The initial state for each simulation was defined as \(x_{0}=[1.1,1.1,1.1,1.1]^{\text{T}}\), expressed in meters (m). The true noise covariance matrices were initialized with \(Q_{true}=\left[\begin{array}{cc}0.5&0\\ 0&1\end{array}\right]\) and \(R_{true}=0.8\), where the units are square meters (\(m^{2}\)). The initial estimations for the noise covariance matrices were set to \(Q_{A}=0.1I_{4}\) for the process noise and \(R_{A}=I_{2}\) for the measurement noise.  
The noise covariance estimates \(\hat{Q}_{ALS}\), \(\hat{R}_{ALS}\) is estimated using the ALS method. Then the steady-state state covariance is computed by solving the Lyapunov equation in (7) as \(P_{e}=\left[\begin{array}{cc}0.32&0.14\\ 0.14&0.35\end{array}\right]\), the steady-state Kalman gain is \(K=[0.17,0.13]\).  
By setting the window size \(N_{w}\) as 5, The empirical estimation of \(N\)-innovations autocovariance is computed as \(\hat{b}=[2.019,1.144,1.177,0.861,0.794]^{\text{T}}\). Then according to the Equation (12), the estimated noise covariance matrix with 500 MC initialization is computed and stored as \(\hat{Q}_{ALS,\text{MC}}\) and \(\hat{R}_{ALS,\text{MC}}\), and the mean value of \(\hat{Q}_{ALS,\text{MC}}\) and \(\hat{R}_{ALS,\text{MC}}\) is \(\hat{Q}_{ALS}=\left[\begin{array}{cc}0.48&0.214\\ 0.214&0.879\end{array}\right]\) and \(\hat{R}_{ALS}=0.801\).  
The effectiveness of the estimated noise covariance matrices, based on 500 Monte Carlo simulations, is depicted in Figures 2 and 3. Figure 2 plots the diagonal elements of the estimated model noise covariance matrix, \(\hat{Q}_{11}\) and \(\hat{Q}_{22}\), on the \(x\)-axis and \(y\)-axis, respectively. Figure 3 presents the estimated measurement noise covariance matrix \(R\) on the \(x\)-axis, with the density of the Monte Carlo simulations displayed on the \(y\)-axis.  
As indicated above, the noise covariance estimates with 500 MC initialization follow the Gaussian distribution where the mean value \(Q_{ALS}\) and \(R_{ALS}\) are near the true value. The results demonstrate that the ALS method can accurately estimate the noise covariance for the KF, and the ALS-KF framework which combines the Kalman filter with the ALS method is well-suited for real-world visual tracking applications where the underlying noise distributions are unknown.
## 3 Numerical Simulation
### Obtaining Mesoscopic Parameters  
#### 3.3.1 Distribution of Damage Forms  
When simulating the specimen loading process, the contact bonds between the SRM specimens will break. The damage evolution process during the specimen loading process can be presented from the contact perspective.  
Figure 10 shows the contact state of the SRM sample during the loading process, with strain ranging from 0% to 10% over three freeze-thaw cycles. The development process of damage cracks and shapes can be seen. In the particle flow program, the contact state pb_state of the parallel bonding model has four values. When the value is 3, it is bonded, and the other values are not bonded or broken or sheared (can be represented in the diagram). Before the simulation starts, the contact state of the sample is red and cemented. When the axial strain reaches 2%, the inter-particle cementation begins to be destroyed, and blue particles begin to appear in the sample, indicating that some of the inter-particle cementation is broken. At this time, it corresponds to the linear elastic growth of stress. When the axial strain reaches 5%, the contact damage between particles further increases, cracks gradually develop and form, and \"X\"-type shear failure begins. At the same time, the specimen begins to fail laterally, but due to the constraints of lateral stress, the middle and lower parts of the specimen begin to bulge outward. After the axial strain reaches 10%, the simulation stops, and the shear cracks are completely formed, extending from the upper left corner to the lower right corner of the sample and from the middle right corner to the lower left corner; the two cracks stagger to form an X-shaped fissure that covers the entire sample, and large lateral deformation occurs on both sides of the model, as shown in Figure 11 compared with the lab test.  
Figure 11: Comparison of sample damage between lab tests and numerical simulations. (**a**) Indoor test specimen. (**b**) Numerical simulation specimen.  
Figure 10: Sample contact damage process.  
Comparing Figure 11a with Figure 11b, it can be seen that the results of the numerical simulation are similar to the lab test. After loading, the specimen state is a shear failure and exhibits a certain lateral bulging deformation. As can be seen from Figure 11b, the failure cracks will bypass the rocks during the development process, create cracks between relatively weak soil bodies, and squeeze out the rocks. At the same time, areas with denser rocks will form a certain skeleton structure, making this area more fragile. Firms are concentrated so that fissures develop between regions.
# On the Kung-Traub Conjecture for Iterative Methods for Solving Quadratic Equations
## 5 Numerical Experiments  
### Scalar Equation  
We consider the **Test problem 1** (TP1) of finding the positive zero of the quadratic function \(f(x)=x^{2}-2\) to compare the efficiency of the proposed methods. Numerical computations have been carried out in the MATLAB software rounding to 1000 significant digits. Depending on the precision of the computer, we use the stopping criteria for the iterative process \(|x^{(k+1)}-x^{(k)}|<\epsilon\) where \(\epsilon=10^{-50}\). Let \(N\) be the number of iterations required for convergence. For simplicity, we denote \(Xe-Y=X\times 10^{-Y}\).  
The computational order of convergence is given by  
\[\rho=\frac{\ln|(x^{(N)}-x^{(N-1)}/(x^{(N-1)}-x^{(N-2)})|}{\ln|(x^{(N-1)}-x^{(N- 2)})/(x^{(N-2)}-x^{(N-3)})|}\]  
We choose \(x^{(0)}=1\). The results in Table 1 show that, as the order of the \((r+2)\)_thBQIM_ I.F. (r = 1,2,3,4,5,6), the methods converge in less iterations. The computational order of convergence agree with the theoretical order of convergence confirming that [PERSON]'s conjecture fails for quadratic functions.  
\begin{table}
\begin{tabular}{c|c c c c c c} \hline \hline
**Error** & **3\(rdBQIM\)** & **4\(thBQIM\)** & **5\(thBQIM\)** & **6\(thBQIM\)** & **7\(thBQIM\)** & **8\(thBQIM\)** \\ \hline \(|x_{1}-x_{0}|\) & 5.6e\(-1\) & 5.8e\(-1\) & 5.8e\(-1\) & 5.8e\(-1\) & 5.9e\(-1\) & 5.9e\(-1\) \\ \(|x_{2}-x_{1}|\) & 2.3e\(-2\) & 7.7e\(-3\) & 2.8e\(-3\) & 1.1e\(-3\) & 4.3e-4 & 1.8e\(-4\) \\ \(|x_{3}-x_{2}|\) & 3.0e\(-6\) & 7.5e\(-10\) & 3.6e\(-14\) & 3.5e\(-19\) & 6.9e\(-25\) & 2.9e\(-31\) \\ \(|x_{4}-x_{3}|\) & 7.0e\(-18\) & 6.9e\(-38\) & 1.3e\(-68\) & 4.0e\(-112\) & 1.8e\(-170\) & 1.3e\(-245\) \\ \(|x_{5}-x_{4}|\) & 8.7e\(-53\) & 4.9e\(-150\) & - & - & - & - \\ \(|x_{6}-x_{5}|\) & - & - & - & - & - & - \\ \(\rho\) & 3 & 4 & 5 & 6 & 7 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the quadratic function \(f(x)=x^{2}-2\) for the 3\(rdBQIM\), 4\(thBQIM\), 5\(thBQIM\), 6\(thBQIM\), 6\(thBQIM\), 7\(thBQIM\) and 8\(thBQIM\) I.F.s.