esa-sceva/satcom-chunk-collection huggingface dataset:
# Repairing Damage Caused by Burrowing Animals in Embarnkments: A Sustainable Proposal
## 3 Results
### One-Dimensional Compression Tests
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test No.** & **Site** & \(\mathbf{e_{0}}\) & \(\mathbf{\varepsilon^{1}_{v}\,[\%]}\) & \(\mathbf{\Delta e^{1}}\) \\ \hline O1 & Casale M. & 1.366 & 0.170 & 0.455 \\ O2 & Casale M. & 0.999 & 0.118 & 0.270 \\ O3 & Casale M. & 0.862 & 0.123 & 0.230 \\ O4 & Casale M. & 0.727 & 0.062 & 0.108 \\ OL12/7 & Casale M. & 0.818 & 0.076 & 0.139 \\ OL13/9 & Casale M. & 0.901 & 0.113 & 0.217 \\ \hline O5 & Remedello & 1.367 & 0.234 & 0.573 \\ O6 & Remedello & 1.158 & 0.210 & 0.514 \\ O7 & Remedello & 1.263 & 0.294 & 0.665 \\ OL14/24 & Remedello & 1.326 & 0.166 & 0.407 \\ OL15/9 & Remedello & 1.218 & 0.132 & 0.295 \\ \hline O8 & Monticelli D. & 1.604 & 0.278 & 0.790 \\ O9 & Monticelli D. & 1.626 & 0.273 & 0.807 \\ O10 & Monticelli D. & 1.315 & 0.263 & 0.608 \\ O11 & Monticelli D. & 1.529 & 0.294 & 0.742 \\ OL16/24 & Monticelli D. & 1.300 & 0.145 & 0.360 \\ OL17/7 & Monticelli D. & 1.267 & 0.128 & 0.292 \\ \hline \hline \end{tabular}  
* Measures were corrected.  
\end{table} 
Table 3: Oedometer test results. Table 4), is clear that the final normalized variation of void index \(\Delta\)e/e\({}_{0}\) is higher for higher CF% soils (Remedello and Monticelli d'Ongina). Hence, the more the CF content is, the more the influence of the 5% lime treatment is clear. By using [PERSON]'s method applied on plots of settlements vs the root square of time during the application of the highest load (225 kPa) for each test, values of the consolidation coefficient Cv were determined and were reported in Figure 10 according to the amount of CF%. For raw samples, a decrease of the consolidation coefficient Cv with the increase of clay fraction is observable while the opposite case is evident for lime treated samples. The difference of the consolidation coefficient Cv between raw and treated samples is more manifest as much as the CF% increases, possibly because of the decrease of the coefficient of volumetric compressibility mv (i.e., increment of stiffness) that is more evident for soils with higher clay amount. Cv is dependent also on permeability k so investigations on the influence of this parameter are mandatory in the next future.
# A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model
## 3 Case Study
### Results  
#### 3.7.1 Model Parameter Optimization  
This experiment, implemented in Python 3.10, uses the Keras 2.10 deep learning library within the Anaconda 3 environment for model construction. The GWO-GRU network model comprises five layers: an input layer, two GRU layers, a hidden layer, and an output layer. The Adam algorithm trains the internal network parameters of the GRU. The number of neurons in the GRU layers, the number of neurons in the hidden layer, the learning rate, the dropout rate, and the training batch size in the GRU network serve as the position coordinates of the wolf pack. The parameter ranges are as follows: the number of neurons in the GRU and hidden layers ranges from 16 to 256, the learning rate ranges from 1 \(\times\) 10\({}^{-4}\) to 1 \(\times\) 10\  
The iterative evolution process of the AE-GWO-GRU model during training is shown in Figure 14. As the number of iterations increases, the fitness value gradually decreases until it stabilizes, achieving convergence after the 125 th iteration. The optimized hyperparameters are detailed in Table 2, while the network structure is shown in Figure 15.  
The GRU model includes an input layer, a first GRU layer with 121 neurons, a second GRU layer with 156 neurons, a hidden layer with 71 neurons, and an output layer, as shown in Figure 15. The dotted line represents the dropout strategy.  
To evaluate the performance of the AE-GWO-GRU prediction model proposed in this paper, four models were selected for comparison: standard GRU, standard BP, AE-GRU,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value Ranges** & **Optimal Value** \\ \hline Number of neurons & 16\(\sim\)256 & 121 \\ in the first layer GRU layer & & \\ Number of neurons & 16\(\sim\)256 & 156 \\ in the second layer GRU layer & & \\ Dropout rate & 0.2\(\sim\)0.5 & 0.2 \\ Number of neurons & 16\(\sim\)256 & 71 \\ in the hidden layer & & \\ Learning rate & \(1\times 10^{-4}\)-\(1\times 10^{-1}\) & 0.007 \\ Batch size & 8\(\sim\)128 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The value of GRU model parameters.  
Figure 14: GWO iterative fitness curve.  
Figure 15: GRU model network structure.  
and AE-BP. The parameters, including the number of neurons and the learning rate of the GRU prediction model, are kept consistent with those of the BP prediction model. Additionally, the input and output parameters remain the same across all models.
## 3 Simulations and Results  
In this section, the methodology introduced in Section 2 is applied to estimate the orbits of continuously ascending Starlink satellites, as well as their equivalent thrust acceleration. Following the estimation results, OP is carried out. The reference orbits, hereafter referred to as the \"true orbits\", are constructed from ephemerides published by SpaceX on the SpaceTrack.org website. These orbits serve as the ground truth for both generating simulated observations and quantitatively evaluating the accuracy of OD and OP. Each SpaceX ephemeris file contains position and velocity data over a three-day period along with associated covariance matrices. These files are updated three times daily at 8 h intervals. To ensure accuracy, the reference orbit is constructed by merging the initial 8 h segments from each ephemeris file, as these segments are characterized by lower uncertainty according to the provided covariance matrices. Simulated radar observations are generated based on the \"true orbits\", ground station coordinates, predefined observation errors, and operational constraints such as range and elevation limits. State propagation is carried out using the reduced dynamics model defined in Equation (4), which incorporates the constant low-thrust acceleration described in Section 2.
## 4 Evaluation Results  
The proposed energy-prediction solution was implemented in a proof-of-concept prototype that has three main components (see Figure 6): (i) the prosumer data integration (i.e., hardware monitoring infrastructure with the data storage), (ii) the preprocessing of raw data, and (iii) the deep-learning models for energy prediction.  
Figure 5: MLP neural network for energy-consumption forecasting using regression.  
From smart energy meters, the data are sent through MQTT queues. It contains the monitored value, the timestamp, the measuring unit, and the sensor ID. An MQTT listener checks for new samples, and all data received are validated and inserted into a MongoDB NoSQL database. The interaction with MongoDB is made with the Mongoose framework that facilitates the data-validation process before inserting it into the database. The weather data (e.g., temperature, humidity, etc.) is collected and used as a feature to improve the energy-demand prediction. We are using periodical requests to weather APIs with the areas in which the smart meters are installed.  
A data-preprocessing thread is associated with each sensor and scheduled to run every hour. The data are converted if necessary and aggregated over one hour, and the missing data are handled by using a multiplicative seasonal method for imputation. The processed database stores the field device configuration, the energy data, and the prediction results. It can store the hourly energy values, the determined clusters, and data needed for model training, such as the daily baseline demand, the valleys, and the peaks. The historical datasets used for evaluation are directly fed into this database. The prediction module implements the prediction algorithms. The K-Means model used for clustering is developed by using the Weka library and the configurable MLP models by using the Deep java library. The input for the MLP is defined as a generic map to enable the model's training with a different number of features.  
We used historical and live energy data streams acquired from the smart meters associated with energy prosumers [51] to evaluate the energy-prediction solution. Combinations of energy and weather features for two categories of prosumers have been considered to determine the energy-prediction accuracy (see Table 1). The models are re-trained on a daily basis, and a thread is scheduled to make predictions in day-ahead (i.e., 24 energy values, one for each hour of the next day).  
Figure 6: Proof-of-concept prototype used in evaluation.
## 5 Experiment and Comparison
### Comparative Analysis
#### 5.2.3 Efficiency of Storage Overhead  
The storage overhead is the excessive usage of the capacity. As per this ABAC scheme, the number of attributes used for the construction of policies for each data is predicted as storage overhead. As per the increase in the number of attributes, the storage overhead also increases; hence to solve this issue, the number of attributes are computed for the generation of access control policy in the AAC-IoT. This selection of number of attributes is not performed in LBAC, and hereby, the difference in the performance of storage overhead is shown in Figure 12.  
From the result outcome, storage overhead is evaluated, and the LBAC attains higher storage overhead than the proposed AAC-IoT. This is because of the consideration of all the attributes in LBAC for the generation of the access policy, whereas it is counted in AAC-IoT using a fuzzy logic method. The designed AAC-IoT system with fuzzy logic takes into account the data type and preference for the computation of the use of the number of attributes to create an access control policy. From Figure 12, the storage overhead in the proposed AAC-IoT is minimized by approximately 20% from the previously implemented LBAC algorithm. This improvement of storage overhead enables the AAC-IoT to enhance the arrival of data for storage.  
Figure 11: Performance of throughput plot against number of transactions.  
Figure 12: Performance of storage overhead plot against number of attributes.  
Overall, the proposed AAC-IoT system model achieves better performances than the existing LBAC. The increase in throughput and the reduction of latency and storage overhead is efficient; hence this model can be used for sensitive applications.
## 1 Introduction
The DMC satellites are in a sun-synchronous circular orbit with an inclination of 98\({}^{\circ}\), operating from an altitude of 686 km and with an overpass of 10.30 am local-time in their ascending mode; the orbital period is about 97.7 min [53]. The first generation DMC satellites acquire data in three spectral channels; near-infrared (Band 1), red (Band 2) and green (Band 3). These channels are similar to Landsat 7 ETM\({}^{+}\) spectral Bands 2 (green), 3 (red) and 4 (NIR) [53]. The first generation DMCsatellites were launched between 2002 and 2003 and have been followed by modified and upgraded systems. The potential that DMC offers for phenological studies is in the daily repeat cycle of image acquisition, coupled with a relatively high spatial resolution. The research presented in this paper aims to investigate whether data from the Disaster Monitoring Constellation of satellites can be used to derive timely quantitative measures of vegetation dynamics at a local scale. The objectives are: (i) to determine whether vegetation indices derived from DMC data can be related to variations in LAI measured at ground level; and (ii) to determine whether these data provide accurate multi-temporal estimates of leaf area index (LAI) for a woodland site in the U.K.
## 6 Implementation and Validation
### Testing
#### 6.2.1 Take the Elevator Challenge  
Figure 10: Take the elevator challenge in SciRoc 2019.  
Figure 9: Robots used as testing platform: RB1 (**left**), Pepper (**center**), and TIAGo (**right**).  
At the beginning of the test, the robot was assigned a target floor, it entered the elevator, letting people first, and every time the door was open, the robot would face a person and ask them for the floor they were on. This sequence was repeated until it reached the destination floor, where the robot came out of the elevator.  
We will show in detail each of the components developed for this test. First, we have a high-level control component implemented as a behavior tree (Tier 1) that deconstructs the entire test into different steps. One of these steps, or sub-mission, are take_elevator. This tree leaf will set the planning layer's goal to execute the necessary actions to reach it. In this case the actions to be executed are check_door, face_person, ask_for_floor and exit_elevator. The first three will be executed sequentially and iteratively until the robot is on the target floor. After this, the exit_elevator action will be executed, and the take_elevator sub-mission will be considered completed. Now let us take a look at how the HRI skill has been integrated into the proposed architecture. The ask_for_floor action is executed when the robot is face to face with a person. This action establishes an arc in the knowledge graph (working memory), which links the robot with the person. The type of the arc is Dialog and would contain _\"ask: Excuse me, what is this floor?\"_. This arc is read by the voice and tablet controller's module. In this way, the robot can ask the question with voice and display the text of this question on the tablet. Then, the _\"ask:...\"_ arc is erased. As a next step, an arc of Dialog type is established with the robot as source and target, which would contain _\"listen:\"_. The dialog system reads this second arc. It sends the audio stream to a cloud system to convert the user's answer into text and extract some information from it, such as the text's intention or, as in this case, the number corresponding to the floor where the robot is located. Finally, a new arc is created with the robot as the origin and the floor's node as the target.
## 3 Proposed Multiple Watermarking Algorithm
### Watermark Extraction and Detection  
The extraction of watermark bits is achieved using the inverse of the process used for embedding watermarks. The procedure for extracting and detecting watermark bits can be summarized as follows:  
1. Let the initialized quantization step size be \(s\), and the value in the embedding process \(s\). Let the index of the extracting watermark be \(k^{\prime}\), and \(k^{\prime}=0\).
2. Initialize the watermark bits' storage array, \(W^{\prime}=\{w^{\prime}[i],\ 0\leq i<N\}\), and let \(w^{\prime}[i]=0\).
3. For the vertices coordinates \(\left(x_{j},y_{j}\right)\), the index of the watermark bit is calculated: \(index=f\left(x_{j},y_{j}\right)\).
4. The potential watermark bits in the vertices, \(wb^{\prime}\), are extracted according to the following Equation (7): \[\left\{\begin{array}{l}wb^{\prime}=-1\ if(d\%(\frac{s}{2^{k-1}}))<\frac{s} {2^{k}}\\ wb^{\prime}=1\ if(d\%(\frac{s}{2^{k-1}}))\geq\frac{s^{2}}{2^{k}}.\end{array}\right.\] (7)
5. Save the extracted watermark bits in \(W^{\prime}\), and \(w^{\prime}\big{[}f\big{(}x_{j},y_{j}\big{)}\big{]}=w^{\prime}\big{[}f\big{(}x _{j},y_{j}\big{)}\big{]}+wb\).
6. For all coordinate vertices, steps (2) to (5) are repeated to extract all watermark sets, \(W^{\prime}\).
7. The relationship between the watermark bits and coordinate vertices is \"many to one\", therefore, let \(w^{\prime}[i]=1\) if \(w^{\prime}[i]>0\). Let \(w^{\prime}[i]=-1\) if \(w^{\prime}[i]<0\), and \(w^{\prime}[i]=1\) or \(w^{\prime}[i]=-1\) randomly, if \(w^{\prime}[i]=0\).
8. The original watermarks are correlated with the extracted watermarks in order to assess whether they are the original watermarks or not. These watermarks are stored if the extracted watermark is the embedded one; otherwise, let \(k^{\prime}=k^{\prime}+1\) and skip to Step (2). The detection is not complete until \(k^{\prime}=k\).
# An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference
## 3 Results  
The simulation analysis is executed in the MATLAB 2016b environment on a desktop computer with an Inter(r) Core i5 3.3 GHz CPU (Intel, Santa Clara, CA, USA), with specific simulation conditions outlined as follows:  
1. Scenario 1: The simulation commences from a starting position with latitude 34.246\({}^{\circ}\), longitude 108.91\({}^{\circ}\), and altitude set at 380 m. The initial speed is 0 m/s, heading north. Following a sequence of actions, including acceleration, turning, and deceleration, the trajectory reaches a destination specified by latitude 34.244\({}^{\circ}\), longitude 108.9\({}^{\circ}\), and altitude of 380 m. The maximum speed during the trajectory is 20 m/s, and the total flight time is 250 s;
2. Scenario 2: The simulation begins at a starting position defined by latitude 34.246\({}^{\circ}\), longitude 108.91\({}^{\circ}\), and altitude of 380 m, with an initial speed of 0 m/s and heading north. Executing actions such as acceleration, turning, and deceleration, the trajectory reaches the destination defined by latitude 34.244\({}^{\circ}\), longitude 108.9\({}^{\circ}\), and altitude of 380 m, then returns to the initial position. The maximum speed during this trajectory is 20 m/s, and the total flight time is 400 s;
3. The IMU initialization data can be acquired through the application of inertial navigation's differential equation based on the designed trajectory information. The constant  
Figure 8: Program flow chart of the AFIS algorithm.  
Figure 7: Schematic diagram of AFIS (the changed variables are shown in red).  
drift and random drift of the gyroscope in INS is 1\({}^{\circ}\)/h and 0.1\({}^{\circ}\)/h. The constant bias and random bias of the accelerometer is 100 \(\upmu\)g and 10 \(\upmu\)g;
4. GPS measurement noise is characterized by a mean square error of 2 m for longitude and latitude, 5 m for height, and 0.2 m/s for speed in all three directions;
5. The initial position of VO is the same as that of the carrier, and the VO position measurement noise is quantified by a mean square error of 1 m;
6. The mean square error of the measurement noise for the Loop factor is 2 m, and the measurement equation is shown in Section 2.1.5;
7. INS sampling frequency is set at 200 Hz, the GPS update frequency at 1 Hz, and the VO output frequency at 1 Hz.
# Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS +
## 4 Experiments and Discussion
### Quantitative Experiments

Table 1: Comparison of success rates (\(\dfrac{\text{number of success complete system}}{\text{number of success S-DOVS only}}\)) and mean time to reach the goal (\(\dfrac{\text{mean time complete system}}{\text{mean time $\text{S-DOVS only}}}\)) in a map with a single room and a map with nine rooms for the same 200 random scenarios for each number of obstacles. The experiments showed that our improved approach outperforms the original version on both metrics, showing the benefits of the added modifications. The proposed approach outperforms the other alternatives as well, as it combines their various attributes with several additional features. In terms of success rate, the improvement is clear. The mean navigation time was only gathered for the successful episodes in every method, meaning that the navigation time metrics are influenced by the success rates. On the one hand, when a method has a low success rate this means that it fails to find a solution in a significant portion of the trials. Thus, the episodes in which it reaches the goal are the simplest ones, which leads to shorter average time to reach the goal. On the other hand, planners with high success rates may spend more time on difficult episodes, leading to higher average time to reach the goal for successful trials. Even with this caveat, our improved approach shows lower navigation times than the two approaches with the most similar success rates. In addition, the results show that the improvements presented in this work clearly improve on the approach presented in our original conference paper [2].
# SC-SA: Byte-Oriented Lightweight Stream Ciphers Based on S-Box Substitution
## 2 SC-SA Cipher
### Randomness Testing  
#### 3.6.1 An 80-Bit Key Avalanche  
The 1024-bit key sequence generated using an all-zero key, represented in hexadecimal, is as follows:  
\"D1 33 A6 F6 64 A E6 91 C6 A4 2E 01 B6 68 21 F7 EA 30 72 3F 05 OD BD AD 74 5E A3 C4 OF 6C A8 FF 5B 42 E7 AB 80 9A 2B 32 56 8A 2B E2 39 70 BB 1D 82 1C EC 10 A7 30 30 26 C9 3E 06 1A CE BF 26 A8 F1 FA 2F 6E 4D A6 8B 54 AA 35 4B 3B 85 BA 1D 24 46 5B 24 2B 3D 3D 82 F7 26 32 25 8D D8 83 60 OD 3C 84 D9 86 A8 35 FB E7 54 AO 53 00 BE 8C DF B6 6B 13 06 33 06 E1 6F C1 AE B3 14 66 16 2C 14\"  
To test how a single bit change in the key affects the ciphertext, a fixed plaintext of all zeros (1,048,576 bits) is used. A random 80-bit key is generated. For each random key, the fixed plaintext is encrypted using the original random key to obtain the original ciphertext. For each position i from 1 to 80, the i-th bit of the original key is flipped to generate a perturbed key. The fixed plaintext is encrypted with the perturbed key to obtain the perturbed ciphertext. The XOR result of the original and perturbed ciphertexts forms the derived block. This process results in 6400 binary sequences, each containing 1,048,576 (128 KB) bits.
# Optical Code Construction of 2D Spectral/Spatial BIBD Codes for SAC-OCDMA Systems
## 2 2D-BIBD Code for SAC-OCDMA Systems  
The construction of a 1D-BIBD code is based on a combinatorial theory that uses cyclic shift method and Galois field for code construction. A BIBD code is represented as (\(L,\ w,\ \lambda_{c},\ \lambda_{a}\)), where \(L\) stands for code length or total time chips in a code-word, \(w\) represents the weight of the code (i.e., time chips having value 1), \(\lambda_{a}\)_is_ the autocorrelation value, and \(\lambda_{c}\) is the maximum cross-correlation value.  
The fundamental equation which governs the BIBD code is given in Equations (1) and (2) [13].  
\[B\times K=V\times R \tag{1}\]  
\[R\times(K-1)=\lambda\times(V-1) \tag{2}\]  
\(B\) represents the total number of blocks and \(K\) is the number of elements in one block, which is equal to the weight of the code (\(w\)). \(V\) is the number of terms that correspond to code temporal length (\(L\)), \(R\) is the replication of each entry, \(\lambda\) is the cross-correlation (\(\lambda_{c}\)) among signature sequences in a code family.  
The design of a 1D-BIBD code consists of two steps: the first step is to design the initial blocks, followed by modulo addition of the initial blocks to get all of the other blocks in a design that is used to reach the second step.  
For a code family of \(N_{max}\) maximum number of users, the length of code \(L\) is given as  
\[L=w\times(w-1)N_{max}+1 \tag{3}\]  
where, \(w\) refers to the code weight, and \(N_{max}\) represents the maximum number of users in the code family or cardinality.  
Therefore, the initial blocks for the BIBD code is given as  
\[\left(\alpha^{0},\alpha^{2 Nmax},\alpha^{4 Nmax}\right),\ (\alpha^{1},\ \alpha^{2 Nmax+1},\alpha^{4 Nmax+1})\,\ldots \ldots\ldots\ldots\ (\alpha^{Nmax-1},\ \alpha^{3 Nmax-1},\alpha^{5 Nmax-1}) \tag{4}\]  
Here \(\alpha\) denotes the primitive element of \(GF(L)\).