esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Evolution Policy-Based Approach for RIS-Enhanced Communication System
Differently from model-based schemes, which rely on gradient descent and backpropagation processes within one single deep neural network (DNN), the proposed DEP-based algorithm generates multiple individuals with diverse characteristics that interact with the environment within a single generation. Specifically, we employ various DNNs to represent different individuals within the population. Following the processes of selection, crossover, and mutation, a new generation comprising multiple individuals will continue to interact with the environment until the performance of the entire population converges. Therefore, the active beamforming at the base station (BS) and the passive beamforming at the RIS can be optimized through the individual with the highest score in the last generation. Furthermore, without processes such as gradient descent and backpropagation, the DEP-based approach can significantly decrease the computation complexity. Ultimately, simulations are conducted to validate our analytical findings.
### Requirements of Underwater Communication
#### 1.4.4 Security and Privacy  
Severe conditions underwater leave the nodes vulnerable to defamatory attacks and threats. Thus, the need to establish trust between the communicating nodes is of utmost interest to preserve data [13].
## 5 Numerical Examples
### Example 6  
In this example, we consider a non-harmonic boundary value problem on an amoebak-like boundary:  
\[\Delta u=u_{xx}+u_{yy}=0,\ \ (x,y)\in\Omega, \tag{137}\] \[u(x,y)=f(x,y)=x^{2}y^{3},\ \ (x,y)\in\Gamma,\] (138) \[\Gamma:=\{\rho(\theta)\cos\theta,\rho(\theta)\sin\theta,\ 0\leq \theta\leq 2\pi\},\] \[\rho=\exp(\sin\theta)\sin^{2}(2\theta)+\exp(\cos\theta)\cos^{2} (2\theta), \tag{139}\]  
where \(\Delta f(x,y)=2y^{3}+6x^{2}y\
eq 0\), and the contour is displayed in Figure 4a by a solid black line. [PERSON] et al. [46] have pointed out that \"non-harmonic boundary data\" mean that the solution does not have a harmonic extension to the whole plane and a solution is very difficult to achieve.  
Let  
\[v(x,y)=u(x,y)-f(x,y) \tag{140}\]  
be a new variable, and then we come to a Poisson equation under a homogeneous boundary condition:  
\[\text{Poisson equation}:\ \left\{\begin{array}{l}\Delta v=F(x,y)=-\Delta f(x,y), \\ v(x,y)|_{(x,y)\in\Gamma}=0,\end{array}\right. \tag{141}\]  
where \(F(x,y)\
eq 0\), because \(f(x,y)\) is a non-harmonic boundary function. When \(v(x,y)\) is solved, we can find \(u(x,y)=v(x,y)+f(x,y)\).  
In order to obtain an accurate solution of \(v(x,y)\), we use the multiple-scale Pascal triangle polynomial expansion method developed by [PERSON] and [PERSON] [47]:  
\[v(x,y)=\sum_{i=1}^{m_{0}}\sum_{j=1}^{i}c_{ij}s_{ij}\bigg{(}\frac{x}{R_{0}} \bigg{)}^{i-j}\bigg{(}\frac{y}{R_{0}}\bigg{)}^{j-1}. \tag{142}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Alg.** & **IP** & \(\|\mathbf{AA^{+}A-A}\|^{2}\) & \(\|\mathbf{A^{+}AA^{+}-A^{+}}\|^{2}\) & \(\|(\mathbf{AA^{+}})^{\mathrm{  
After collocating \(q\) points to satisfy the governing equation and boundary condition (141), we have a non-square linear system (2), where the scales \(s_{ij}\) are determined such that each column of the coefficient matrix \(\mathbf{A}\) has the same norm.  
By using the DOA, we take \(m=4\), \(q=200\), \(m_{0}=5\), \(R_{0}=1000\) and \(c_{ij}^{0}=0\). Hence, the dimension of \(\mathbf{A}\) is \(200\times 15\), for which system (2) is an over-determined system. The distribution of collocation points is shown in Figure 4a, while the convergence rate is shown in Figure 4b. It is amazing that the DOA converges with nine steps even under a stringent convergence criterion \(\varepsilon=10^{-10}\). In Figure 5a, we compare the recovered boundary function with the exact function at 1000 points along the boundary. They are almost coincident, and thus we plot the absolute error in Figure 5b, whose ME = \(2.95\times 10^{-12}\) and the root mean square error (RMSE) = \(1.2\times 10^{-12}\). This result is much better than that computed by [PERSON] et al. [46].  
Figure 4: An amoeba-like boundary under a non-harmonic boundary function solved by the DOA: (**a**) the contour and distribution of collocation points, and (**b**) the convergence rate.  
Figure 5: An amoeba-like boundary under a non-harmonic boundary function solved by the DOA: (**a**) comparison of numerical and exact boundary conditions, and (**b**) the numerical error.
## 3 Wireless Localization Technology
### Range-Based Techniques
#### 3.1.3 Time Difference of Arrival (TDoA)  
TDoA is the difference between the distances between a device and two anchors, divided by the speed of light, and estimates the location of the device by finding the intersection of the parabolic curves with the two anchors as the focus, as shown in Figure 4. This technology is used even when synchronization between the device and the network is not established, by simply obtaining the time difference from multiple anchors. On the other hand, recent research has aimed at translating the time difference of arrivals for two signals transmitted at different frequency bands simultaneously into the distance to the receiving device.  
Figure 3: An illustration of the concept of OWR and TWR.
# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 3 Proposed Method
### Cluster Head Selection Phase
#### 3.3.1 Improved Fitness Function for CH Selection  
In order to increase the network lifetime, a set of optimal CHs should be selected; if the number of CHs were too low, nodes would have to increase their communication range to send the information, and so they would consume more energy. However, if the number of CHs were too high, the network would be a single hop, again leading to increased energy consumption. Therefore, it is necessary to determine the optimal criteria of the fitness function based on the application [26]. In this paper, the fitness function in WSN is a multi-objective function that needs to be minimized to reach optimal or near-optimal solutions. Additionally, three criteria have been used to select CHs. First, the energy-efficiency criterion (EE) is included, inspired by Equation (3) of the Hoang method [41], as mentioned above; then, two other criteria are added to the fitness function, which are called cluster closeness (CC) and network coverage (NC).
# A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability
## 7 Discussion
### Discussion about the Prediction Accuracy of the Grey-Box Model  
In terms of accuracy, our proposed Grey-Box model outperformed White-Box model in most cases of our experiments especially on our educational datasets. As regards, Coimbra dataset, the two models reported almost identical performance with Grey-Box to be slightly better. Relative to Winsconsin dataset there were two cases in which the White-Box model reported better performance over the Grey-Box. Probably this occurs due to the fact that these datasets are very small (few number of labeled instances) and becoming even smaller by the self-training framework as the datasets have to be split in labeled and unlabeled datasets.  
Most of Black-Box models are by nature \"data-eaters\", especially neural networks, meaning that they need a large number of instances in order to outperform most of White-Box models in terms of accuracy. Our Grey-Box model consists of a Black-Box base learner which was initially trained  
Figure 14: Box-plot with performance evaluation of Grey-Box and Black-Box models for Winsconsin dataset.  
on the labeled dataset and then makes predictions on the unlabeled data in order to augment the labeled dataset which is then used for training a White-Box learner. If the initial dataset is too small, there is the risk that the Black-Box could exhibit poor performance and make wrong predictions on the unlabeled dataset. This implies that the final augmented dataset will have a lot of mislabeled instances forcing the White-Box, which is the output of the Grey-Box model, to exhibit low performance too. As a result, the proposed Grey-Box model may have lower or equal performance compared to the White-Box model.  
Moreover, the Grey-Box model performed slightly worse than the Black-Box models as expected since our goal was to demonstrate that the proposed Grey-Box model achieved a performance almost as good as a Black-Box model. Thus, in summary our results reveal that our model exhibited better performance compared to the White-Box model and reported comparable performance to the Black-Box model.
## 6 Simulations
### Results and Discussions  
#### 6.3.1 Performance Evaluation of the Supervised Learning-Based Method  
Before conducting the simulation of the semi-supervised and enhanced indoor positioning under limited labels, we first evaluate the performance of positioning under a supervised learning situation, which uses the full labels in the fingerprint dataset and an original CIR input without augmentation. For an objective comparison, we also use other fingerprinting methods based on deep learning and a traditional estimation method to test their positioning accuracy under the heavy NLOS scenario.  
The full number of labeled data for input is 80,000. In the supervised learning trial, we also use the rate of 2% to divide the training set and test set. In CIR-based fingerprint indoor positioning, we use the ResNet structure mentioned in 5.1 to conduct the simulation. In the training process, the batch size and learning rate are set to 512 and 0.1 respectively; the adam optimizer and cosine-annealing scheduler are also used in the training.  
Meanwhile, we also process the TOA, TDOA, and RSS fingerprints that are collected in the same scenario with the same collecting steps to deal with the positioning task using a learning-based method. The structure of a single datum is 1 \(\times\) 18, which stands for the TOA/TDOA/RSS signal received from 18 BSs. These fingerprint datasets are also listed in WLL_3_1015_Inf_DH662_FR1_drop1_cir_rsrp_toa_tdoa from [59]. To avoid destroying the dimension of these fingerprints, we applied a Multi-layer Perception(MLP) network to recognize the features. The MLP structure consists of 4 linear layers with an activation function ReLU connected to each linear layer and a dropout operation before the last linear layer.  
Additionally, we also apply CHAN, a traditional method that is widely used in TDOA-based positioning [21]. By utilizing the TDOA fingerprint values and the known  
Figure 6: Structure of Resnet.  
BS positions to apply the Weighted Least Squares (WLS) algorithm, we can obtain the calculated position of UEs, noting that the algorithm is under the case of more than three reference nodes.  
The position performance can be reflected by the CDF of positioning errors and Mean Error (ME), which are shown in Table 3. By comparing the performance of the learning-based fingerprint positioning and CHAN, we can conclude that AI positioning methods are superior to the traditional TDOA-based estimation algorithm. In the performance of learning-based fingerprint positioning, all displayed methods reach the accuracy of sub-meter level, while the CIR-based method has the best positioning accuracy, which reaches an ME of 0.31 m.  
After the performance evaluation of CIR-based fingerprint positioning, we can observe that it outperforms positioning methods based on other measurements collected from the same scenario; thus, it is more effective to use it as the basic module in our proposed training framework.
## 4 Network and Strategy for Scheduling
### Scheduling Strategy  
#### 4.5.1 Static Scheduling Strategy  
Static scheduling is typically used when multiple workflows are submitted at once, and there will be no new submissions thereafter. This allows the network to consider scheduling from a holistic perspective. As a result, certain sub-tasks can be strategically deferred or advanced to optimize the overall response time.  
In the normal case, there may be multiple optional sub-tasks for the current timestamp, and the network performs regular scheduling. However, if there are no sub-tasks available for scheduling (due to unfinished predecessor sub-tasks or insufficient resources), a state update process is designed to handle this scenario. In such cases, the timestamp is shifted by the time interval \(t_{avg}\), where \(t_{avg}\) represents the average execution time of all tasks in the workflow. Additionally, resources occupied by tasks that have completed their execution at the current timestamp are released. By employing this update strategy, the network is able to consider multiple tasks within a short time period and make optimal decisions. However, if there is still only one sub-task available for scheduling after applying this update, the network has no choice but to schedule that particular task. The decoding process, including the state update, is outlined in Algorithm 1. In cases where the Decoder's output is null, the mentioned state updating strategy needs to be applied to enable the decoder to continue decoding.  
```
1:while not terminated do
2:\(a\Leftarrow\) output of Decoder;
3:if\(a!=null\)then
4: Add \(a\) to \(running\_queue\);
5: Update workflow state
6: Reduce \(R_{edge}\);
7:else
8:\(t\Leftarrow t+t_{avg}\) ;
9: Remove the completed sub-tasks from \(running\_queue\);
10: Release the resources corresponding to sub-tasks;
11:endif
12: update mask sequence;
13:endwhile
14:return sub-tasks sequence;
```  
**Algorithm 1** Static scheduling strategy  
It is important to note that, despite the network selecting a sub-task, it does not imply immediate execution of the sub-task at that timestamp. This is because the timestamp is directly shifted forward. The actual execution time of a sub-task is determined at runtime. The evaluation of scheduling results is based on the actual execution outcomes and is fed back to the network through training.
## 4 Results and Discussion
### Speciation Distribution Characteristics of Heavy Metals
The As in the soil of each sampling point is mainly a residual fraction, and the potential ecological risk to the soil environment is minimal, which is different from the traditional view that gold mining areas easily cause As pollution [42]. Although the proportion of reducible fraction at sampling points 12, 14 and 33 exceeded 20%, and the proportion of oxidizable fraction at sampling points 16 and 33 reached 17.40% and 14.47%, respectively, the proportion of these two forms was far lower than that of residual fraction. Cd mainly exists in the residual fraction, followed by the mild acid-soluble fraction in the soils of the sampling points in the study area, and the proportion of the mild acid-soluble fraction reached 21.29% in the sampling point 19, which needs to be emphasized. In the study of heavy metal pollution in the mining area at home and abroad, Cd pollution widely exists and its biological effectiveness is very high, so further investigation into Cd pollution in our research area is imperative. In domestic and international studies of heavy metal pollution in mining areas, Cd pollution is widespread and highly bio-effective, and in-depth studies of Cd pollution in the study area should be carried out. The speciation of Pb in the soil of each sampling point in the study area was dominated by residual and reducible fractions, and the mild acid-soluble fraction accounted for a relatively small amount, this is due to the large presence of anions such as CO\({}_{3}\)\({}^{2-}\) and OH\({}^{-}\) in the soil will form ferromanganese oxides with Fe and Mn, which have a strong adsorption capacity of pb\({}^{2+}\), and the Pb and the ferromanganese oxides are combined to form a stable complex, which is difficult to be released for migration [41; 43]. The water-soluble fraction of Hg is not detected in all sampling sites in the study area, and the mild acid-soluble fraction of Hg is not detected in some of the sampling sites, and the main storage form was the residual fraction. Sampling site No.1 is different from the other sites in that the oxidizable fraction accounted for a higher percentage of the total, but the soil in the study area is acidic, and the oxidizable fraction is more stable, so the ecological hazards are lower. Figure 3: The percentage diagram of heavy metal soil speciation in the study area.
###### Abstract  
This study aimed to uncover the chronology and production technologies of ancient bricks unearthend from various locations in Upper Assam, Northeast India. To achieve this goal, complementary spectroscopic techniques such as Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Scanning Electron Microscope (SEM) coupled with Energy Dispersive Spectrometer (EDS), as well as Thermoluminescence (TL) and Optically Stimulated Luminescence (OSL/IRSL) dating, were applied. FTIR and XRD analyses revealed the presence of quartz, feldspar (microcline, orthoclase, albitte), kaolinite, chlorite, corussite, palyorskite, magnetite, hematite, and organic carbon. The mineralogical composition indicates two distinct groups with firing temperatures below 650 \({}^{\circ}\)C and above \(\sim\)800 \({}^{\circ}\)C. These two groups could be the first indication of the presence of two civilizations or at least two different production technologies involving different firing temperatures and kiln atmospheric conditions. Further, the SEM-EDS study suggests that both calcareous and non-calcareous clays were used in brick making, which have low and high refractory properties, respectively. The internal morphology of the samples shows the existence of micropores and microfractures, indicating the influence of higher-temperature firing. Absolute dating techniques associate the two brick groups with different age ranges: a firing temperature above \(\sim\)800 \({}^{\circ}\)C indicates a superior technology corresponding to a production period between the 7 th and 10 th centuries CE. In contrast, a temperature below 650 \({}^{\circ}\)C indicates a technologically less advanced group of people, with the age group dated between the 11 th and 14 th centuries CE.  
Keywords:firing temperature; XRD-FTIR; SEM-EDS; archaeometry; ancient bricks; TL/OSL dating +
Footnote †: journal: Applied Sciences
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## Abbreviations  
1D 1 dimension  
3D 3 dimensions  
3D-MLAOMP 3D-MMV look ahead orthogonal match pursuit  
3D-MMV  Three-dimensional multiple measurement vector  
6G  Sixth-generation  
A2C  Asynchronous Advantage Actor-critic  
AB-HBF  Angular-based Hybrid Beamforming  
ADMM  Alternating Direction Method Of Multipliers  
ALS  Alternating Least Squares  
AO  Alternating Optimization  
AoA  Angle of Arrival  
AoD  Angle of Departure  
BB  Baseband  
BS  Base Station  
CPI  Conservative Policy Iteration  
CPU  Central Processing Unit  
CS  Compression Sensing  
CSI  Channel State Information  
DE  Deterministic Equivalent  
DFT  Discrete Fourier Transform  
DQN  Deep Q-networks  
EM  Expectation Maximization  
FDD  Frequency Division Duplex  
GAE  Generalized Advantage Estimator  
HBF  Hybrid Beamforming  
HOSVD  Higher-order Singular Value Decomposition  
HRIS  Hybrid RIS  
I-CSI  Instantaneous CSI  
IoVs  Internet of Vehicles  
JCEDD  Joint Channel Estimation and Data Detection  
KL  Kullback-Leibler  
LMMSE  Linear MMSE  
LoS  Line of Sight  
LS  Least Squares  
LTI  Long-term Imperfection  
MDP  Markov Decision Process  
MIMO  Multiple Input-Multiple Output  
MISO  Multiple Input-Single Output  
mMIMO  massive Multiple Input-Multiple Output  
MMSE  Minimum MSE  
MMV  Multiple Measurement Vector  
MP  Message Passing  
MSE  Mean-Squared-Error  
MU-MISO  multi-user MISO  
NLoS  Non-line-of-sight
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
###### Abstract  
This paper presents a high-speed and low-area accelerator architecture for shared key generation using an elliptic-curve Diffie-Hellman protocol over \(GF(2^{233})\). Concerning the high speed, the proposed architecture employs a two-stage pipelining and a Karatsuba finite field multiplier. The use of pipelining shortens the critical path which ultimately improves the clock frequency. Similarly, the employment of a Karatsuba multiplier decreases the required number of clock cycles. Moreover, an efficient rescheduling of point addition and doubling operations avoids data hazards that appear due to pipelining. Regarding the low area, the proposed architecture computes finite field squaring and inversion operations using the hardware resources of the Karatsuba multiplier. Furthermore, two dedicated controllers are used for efficient control functionalities. The implementation results after place-and-route are provided on Virtex-7, Spartan-7, Artix-7 and Kintex-7 FPGA (field-programmable gate arrays) devices. The utilized FPGA slices are 5102 (on Virtex-7), 5634 (on Spartan-7), 5957 (on Artix-7) and 6102 (on Kintex-7). In addition to this, the time required for one shared-key generation is 31.08 (on Virtex-7), 31.68 (on Spartan-7), 31.28 (on Artix-7) and 32.51 (on Kintex-7). For performance comparison, a figure-of-merit in terms of \(\frac{throughput}{area}\) is utilized which shows that the proposed architecture is 963.3 and 2.76 times faster as compared to the related architectures. In terms of latency, the proposed architecture is 302.7 and 132.88 times faster when compared to the most relevant state-of-the-art approaches. The achieved results and performance comparison prove the significance of presented architecture in all those shared key generation applications which require high speed with a low area.  
high speed; low area; cryptoprocessor; accelerator architecture; ECDH; FPGA acronymsics
## 6 Discussion
### Performance and Effectiveness  
The empirical results from our case study with Carrefour and Monoprix demonstrate the system's effectiveness in real-world scenarios. The integration of temporal analysis with geographic data provides more contextually relevant recommendations than traditional systems. Particularly noteworthy is the performance of the Haversine formula-based distance calculations combined with user ratings, which proved highly effective for store recommendations. This approach successfully balances proximity with user preferences, as evidenced by the scoring system's performance across different store locations. Similar conclusions on the significance of geographic information in recommendation systems have been made by [26], who stress that location-aware algorithms prioritize closeness in order to increase user happiness. Additionally, research by [27] supports our findings about the influence of customer behavior patterns over time and emphasizes the need of incorporating temporal aspects into recommendation systems.