esa-sceva/satcom-chunk-collection huggingface dataset:
# An FPGA-Based Performance Analysis of Hardware Caching Techniques for Blockchain Key-Value Database
## 6 Conclusions  
Efficient use of caching for quick data access is a major challenge in Blockchain. In particular, for a key-value database used in Blockchain, the caching system can improve the number of transactions handled by the full nodes. In this paper, we implement different caching schemes on FPGA NIC and analyze their performance in terms of metrics such as cache hit rate and cache miss rate. We designed 2-way and 4-way cache techniques on BRAM and compared them with the direct-mapped cache technique. Although set-associative cache systems increase cache complexity, they improve the cache hit ratio. The improvement in the hit ratio of the 2-way set-associative cache technique with respect to the direct-mapped cache technique for 10 K, 25 K, and 50 K keys are 0.8%, 0.77%, and 1.67%, respectively. Meanwhile, the same sets of keys, the hit rate improved by the 4-way set-associative cache technique with respect to the direct-mapping cache technique is 0.92%, 2.01%, and 2.4% respectively. The improvements in hit rate for large data sets show that 2-way and 4-way set-associative cache techniques perform better than direct-mapped cache techniques for caching systems. In the future, when a multilevel cache system is used  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Address** & **2-Way** & **4-Way** \\ \hline
**10 K** & 0.8\% & 0.92\% \\ \hline
**25 K** & 0.77\% & 2.01\% \\ \hline
**50 K** & 1.67\% & 2.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Hit rate improvement in direct mapping with respect to time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Direct-Mapped** & **2-Way** & **4-Way** \\  & **Set-Associative** & **Set-Associative** \\ \hline
**DSP Slices** & **1** & **1** & **1** \\ \hline
**RAMs** & **1011** K & 2022 K & **4044** K \\ \hline
**Slice LUTs** & **38,076** & **49,808** & **71,540** \\ \hline
**Slice Registers** & **6890** & **8970** & **15,898** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Resource utilization comparison.  
Blockchain, BRAM can act as a level one cache and RLDRAM can act as a second-level cache to accelerate the caching delay. Moreover, we will investigate the use of multiple FPGAs in parallel for accelerating the Blockchain mining operation.  
This article was prepared through the collective efforts of all the authors. Conceptualization, M.F.S., F.A., M.A.J., M.B.K., A.K.J.S., M.A. and M.H.A.H.; writing--original draft, M.F.S., F.A. and M.A.J.; writing--review and editing, M.B.K., A.K.J.S. and M.A. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University for funding this work through Research Group no. RG-21-07-07.  
The authors declare no conflict of interest.
## 1 Introduction  
Shoulder injuries are among the most common occupational hazards: fractures, dislocations, rotator cuff fears and other soft-tissue injuries often require surgeries and several months of rehabilitation therapy. At the same time, loss of function at shoulder level leads to significant impairment in daily life activity due to the crucial role of the shoulder complex in allowing for hand movements in the workspace.  
Moreover, the shoulder complex has the greatest mobility of all joints in the human body [1] thanks to an intricated set of muscles, tendons, ligaments, bones and joints [2] working in a coordinated and synchronous way to offer a constant compromise between mobility and stability and, consequently, this poses real challenges when it comes to rehabilitation [3].  
In this perspective, robotic rehabilitation can be a valid tool to provide a more repeatable and measurable therapy compared to conventional therapies. In fact, in the last decade, several exoskeletons for shoulder rehabilitation have been presented in the literature [4, 5, 6, 7, 8, 9].  
Most of these devices focus on the shoulder complex and limit the normal mobility of the rest of the body, forcing the patient into a fixed standing or sitting position.  
Indeed, most of the exoskeletons presented so far were designed for neurologic patients and thus a fixed position may be useful especially in subjects with severe impairment or in the early rehabilitation phases.  
Conversely, engagement in occupational activities is usually introduced later in the patient's care pathway. Nevertheless, this classical rehabilitation approach severely limits the range of activities that can potentially be simulated during the first rehabilitation phases. Indeed, several activities of daily living (ADLs) involve different muscular groups and whole-body movements; from relocating or moving objects in the space, to sideways reaching or picking objects from the ground. It has been proven that psychologically rewarding exercises, as those included in occupational therapy, improve the patient's motivation [10]. Furthermore, the capability of full body motion to engage people has already been demonstrated in fields such as digital gaming [11; 12], highlighting the potential benefits deriving from physical-social interactions as well.  
To enable a careful whole-body functional rehabilitation [13], the challenge is to shift the paradigm of robotic rehabilitation towards machines that can enable wide workspaces and high mobility in ecologically valid scenarios [14]. In this perspective, here we present Float: an upper-limb exoskeleton designed to promote and accelerate the motor and functional recovery of the shoulder joint complex following post-traumatic or post-surgical injuries. The key component is a passive polyarticulated arm which supports the total exoskeleton weight and allows the patient to move freely in space. Float empowers rehabilitation through a deeper interaction with the surrounding environment compared to other rehabilitation exoskeletons presented in literature [4; 5; 6; 7; 8; 9].
## 2 Research Data and Research Methods
### Research Data
#### 2.2.4 Soil Moisture Data  
The soil moisture data employed in this study were sourced from the ERA5-Land reanalysis dataset provided by the Copernicus Climate Change Service (available at [[https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=download](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=download)]([https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=download](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=download)), accessed on 11 November 2024). This dataset, based on advanced meteorological and climatological models, provides monthly soil moisture data from 1950 to 2023. It constitutes an essential tool for examining spatial and temporal variations in soil moisture. However, it is important to note that ERA5-Land has documented biases in high-elevation and arid regions. Using the Geographic Information System (GIS) platform, the raw dataset was processed and converted to an annual scale to support long-term trend analysis. For this study, soil moisture data for the period 2001-2020 at four depths (0-50 cm [SM1], 50-100 cm [SM2], 100-150 cm [SM3], and 150-200 cm [SM4]) were selected. The spatial resolution was resampled to 1 km \(\times\) 1 km using the nearest neighbor method to match the scale requirements of the study area.
## 6 Results and Discussion
### Understanding the FSF Performance
Regarding the delivery delay and the average cost, the behaviour did not change: the larger the buffer size, the longer the average message delivery delay and the average cost. Again, a reasonable justification relates to the increase in the delivery ratio. The algorithm FSFwSN had a lower average delivery delay, and a lower average cost as well. Figure 14: Delivery ratio when varying the buffer size. Figure 13: Average cost when increasing the message TTL. The results show that the presence of selfish nodes strongly impacts the performance of the FSF algorithm and the other ones found in the literature. However, taking into account the results of FS + BM, it is reasonable to assume that the management of factors like buffer and energy can mitigate the impact of selfish nodes.
## 4 Experimental Results
### Comparison of the HSAN with Other Models  
Table 8 presents the evaluation results for all classification models in the test set. The HSAN model was superior to the two traditional ML models and nine DL models evaluated, with the \(F1_{overall}\) being 58.94% by SHAN model. The labeling ratios of the training, validation and test datasets were unbalanced, and the labeling ratio of the training set versus the validation and test sets was inverted. Therefore, the \(F1_{overall}\) measurement was used to evaluate other models on the test dataset. The performance of six models such as the TextCNN, TextRCNN, DPCNN, Transformer, CapsuleNet, and the HAN models were similar but not superior to those of the HSAN. The differences compared to the HSAN model in those six models in the \(F1_{overall}\) values were 0.37%, 0.41%, 0.87%, 8.37%, 22.9%, and 3.98%. To compare with the three BERT classifiers (such as _bert-base-chinese_, _distilbert-base-multilingual-cased_, and _allenai/longformer-base-4096_) that our proposed HSAN model also obtains better performances, the differences in the \(F1_{overall}\) were 6.59%, 4.94%, and 14.45%, respectively. The _longformer_ pretrained model supports sequences of length up to 4096 words, but cannot obtain a better classification performance. In another hand, the HSAN model also outperforms machine learning models such as SVM and RandomForest, which the difference in the \(F1_{overall}\) are 6.44% and 15.78%, respectively. In summary, the TextCNN model has the smaller difference in the \(F1_{overall}\) compared with HSAN, and CapsuleNet has a larger difference in the \(F1_{overall}\). In addition, the TextRCNN model outperforms the BERT and Transformer models. A reason is that the TextRCNN has considered all words (average 1380 words) of the input content ([PERSON] et al., 2015); in this case, the pretrained BERT model has only considered 512 tokens (words) which is not enough to cover the whole input content (i.e., average 1380 words in a cyberbullying document). Therefore, the pretrained BERT model applied to the cyberbullying problem with the long document is not obtained the effective learning, because most of the words are not covered.
# Integrated Hydrological Analysis of Little Akaki Watershed Using SWAT-MODFLOW, Ethiopia
## 2 Materials and Methods
### Groundwater Modelling Method and Procedures
#### 2.3.7 MODFLOW Model Calibration  
A steady-state calibration was carried out for the model based on the groundwater level data available. The MODFLOW model was calibrated using an automatic calibration tool, PEST for hydraulic conductivity using the observed water table and estimated base-flow at the out late of the watershed. It is assumed that the stream baseflow (separated from the runoff component of the river streamflow) is sourced from the groundwater flow to the river. Based on this assumption, the groundwater flow model, MODFLOW is calibrated with baseflow obtained from streamflow, in addition to calibration with few available groundwater observation data, to improve the model performance.  
The primary effective calibration practice for the fine-tuning of the hydraulic conductivity field in the model was to initially define fewer conductivity zones and then progressively raise the number of zones based on the available geomorphology of the area. The hydraulic conductivity values were continually adjusted during calibration in each hydraulic conductivity zone.
## 3 Results and Discussion
### Principal Component Analysis
PCA \"ignores\" class labels and its purpose is to find the directions (the principal components) that maximize the variance in a dataset, and it can be considered an \"unsupervised\" algorithm. In contrast to PCA, LDA is \"supervised\" and computes the directions (\"linear discriminants\") that will represent the axes that maximize the separation between multiple classes. Generally, for a multi-class classification task where the class labels are known, LDA is considered a more robust technique. For this reason, LDA was applied to our data set.
## 2 Basic Theory of Signal Noise Reduction Method
### New Improved Threshold Wavelet Method  
In order to improve the noise reduction effect and reduce the signal distortion, an improved threshold wavelet method is proposed. The improved threshold function is shown as Equation (5)  
Figure 2: Wavelet threshold noise reduction flow chart.  
\[B=\left\{\begin{array}{l}\text{sign}(A)[(|A|-\frac{\lambda^{2}}{|A|\exp(|A|^{2}- \lambda^{2})})],|A|\geq\lambda\\ 0,|A|<\lambda\end{array}\right. \tag{5}\]  
where \(B\) is the wavelet coefficient after threshold processing, \(A\) is the wavelet coefficient after decomposition, \(\lambda\) is the threshold value.  
The hard, soft and newly improved threshold function curves are shown in Figure 3. The \(B\) value for the improved method is situated between soft and hard cases so that the estimated wavelet coefficient is close to \(A\). This improved kind of threshold processing method overcomes the discontinuity at \(\lambda\) compared to the hard method and the unavoidable signal deviation compared to the soft method.
## 3 Proposed System
### Loop Closure Detection
#### 3.3.4 Temporal Consistency  
After obtaining the best match island \(\mathrm{Y}_{t_{j}}\), a simple temporal consistency test is performed. This test is based on previously observed matching islands, and for the current image \(I_{t}\), its best matching island \(\mathrm{Y}_{t_{j}}\) must have a significant overlap with the best matching islands detected in the time index interval of \(t\) and \(t-k\). A simple example of the temporal consistency verification process is shown in the Figure 4.  
If an island \(\mathrm{Y}_{t_{j}}\) passes the temporal constraint, the image \(I_{t}^{\prime}\) with the highest score \(\widetilde{s}(v_{t},v_{t}^{\prime})\) in \(\mathrm{Y}_{t_{j}}\) is considered a real loop closure. The temporal consistency test helps to reduce false positives and improve the accuracy of loop closure detection.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Standard Solution** & **Retention Time** & **Calibration Curve** & **R** & **LOD (mg mL\({}^{-1}\))** & **LOQ (mg mL\({}^{-1}\))** \\ \hline Fructose & 5.688 (\(\pm\)0.002) & y = 42967x + 2390 & 0.999 & 0.022 & 0.074 \\ Glucose & 6.355(\(\pm\)0.006) & y = 39267x + 2667 & 0.999 & 0.030 & 0.099 \\ Sucrose & 8.557(\(\pm\)0.001) & y = 43333x + 3347 & 0.998 & 0.017 & 0.058 \\ Turanose & 9.311(\(\pm\)0.005) & y = 51050x \(-\) 24388 & 0.986 & 0.006 & 0.021 \\ Maltulose & 9.689(\(\pm\)0.002) & y = 43706x \(-\) 6540 & 0.991 & 0.007 & 0.025 \\ Maltose & 10.001(\(\pm\)0.012) & y = 40876x \(-\) 7818 & 0.995 & 0.007 & 0.024 \\ Trehalose & 10.907(\(\pm\)0.009) & y = 48163x \(-\) 28102 & 0.989 & 0.009 & 0.031 \\ Isomaltose & 11.811(\(\pm\)0.016) & y = 33258x \(-\) 18194 & 0.984 & 0.010 & 0.033 \\ Melibiose & 12.858(\(\pm\)0.024) & y = 33214x \(-\) 21648 & 0.985 & 0.018 & 0.060 \\ Erlose & 13.127(\(\pm\)0.015) & y = 58367x + 46836 & 0.967 & 0.005 & 0.017 \\ Melezitose & 14.308(\(\pm\)0.034) & y = 46332x \(-\) 13694 & 0.990 & 0.007 & 0.025 \\ Raffinose & 16.343(\(\pm\)0.038) & y = 2592x \(-\) 26104 & 0.997 & 0.016 & 0.053 \\ Maltotriose & 16.836(\(\pm\)0.023) & y = 38501x \(-\) 17603 & 0.998 & 0.012 & 0.040 \\ Panose & 19.238(\(\pm\)0.031) & y = 59823x \(-\) 80025 & 0.965 & 0.024 & 0.080 \\ Isomaltotriose & 23.126(\(\pm\)0.028) & y = 25926x \(-\) 26104 & 0.975 & 0.029 & 0.096 \\ Maltotetraose & 27.909(\(\pm\)0.039) & y = 42102x \(-\) 45354 & 0.979 & 0.025 & 0.085 \\ \hline \end{tabular}
\end{table} 
Table 2: Average retention time (\(\pm\)sd), equations, correlation coefficient, LOD (mg mL\({}^{-1}\)) and LOQ (mg mL\({}^{-1}\)) of the 16 studied sugars. [PERSON] et al. [3] found that the adulteration with glucose-fructose-maltose syrups resulted in modifications of the honey samples' sugar composition. Furthermore, [PERSON] et al. [26] noted that increasing sucrose syrup in the feeding resulted in a significant decrease in glucose and fructose contents. On the other hand, according to [PERSON] and [PERSON] [27], feeding colonies with small amounts of inverted syrups had a significant effect on the indicated honey's fructose and glucose levels. Additionally, supplements with a high concentration of sucrose (candy paste and sugar syrup) resulted in samples with a significant amount of the specific sugar, which many times exceeded the established limit of 5% [28]. Similar results are reported by [PERSON] et al. [29]. Regarding the other sugars, the control group showed significantly higher concentrations of trehalose and melezitose. Samples from groups with inverted syrup had
# Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar
###### Abstract  
Battlefield surveillance radar is usually 2-D radar, which cannot realize target three-dimensional localization, leading to poor resolution for the air target in the elevation dimension. Previous researchers have used the Traditional Height Finder Radar (HFR) or multiple 2-D radar networking to estimate the target three-dimensional location. However, all of them face the problems of high cost, poor real-time performance and high requirement of space-time registration. In this paper, Reconfigurable Intelligent Surfaces (RISs) with low cost are introduced into the 2-D radar to realize the target three-dimensional localization. Taking advantage of the wide beam of 2-D radar in the elevation dimension, several Unmanned Aerial Vehicles (UAVs) carrying RISs are set in the receiving beam to form multiple auxiliary measurement channels. In addition, the traditional 2-D radar measurements combined with the auxiliary channel measurements are used to realize the target three-dimensional localization by solving a nonlinear least square problem with a convex optimization method. For the proposed RIS-assisted target three-dimensional localization problem, the Cramer-Rao Lower Bound (CRLB) is derived to measure the target localization accuracy. Simulation results verify the effectiveness of the proposed 3-D localization method, and the influences of the number, the positions and the site errors of the RISs on the localization accuracy are covered.  
2-D radar; three-dimensional localization; reconfigurable intelligent surface; least square estimation; Cramer-Rao Lower Bound 2024 16, 1936. [[https://doi.org/10.3390/rs16111936](https://doi.org/10.3390/rs16111936)]([https://doi.org/10.3390/rs16111936](https://doi.org/10.3390/rs16111936))
## 5 Simulation and Experimental Results  
A comparative study has been carried out to show the efficacy of the proposed controller. In Example 1, EGESO based SMC is compared with other control techniques [13; 14]. The proposed controller is also applied to Example 2, an uncertain, unstable system with minimum phase zero, and the results are compared with other techniques. Finally, the proposed controller is validated with other controllers on SLFM actuated by DC motor. Mismatched disturbances are introduced in all cases and removed actively from the output with the help of the proposed control law.
## 2 Leakage Point Detection and Groundwater Level Monitoring
### Groundwater Level Monitoring near the Seepage Point  
The distributed optical fiber field test shows that the temperature variation was small in temperature at depths of 0-24 m of the foundation pit, and was within 7.5-10 \({}^{\circ}\)C. The temperature dropped significantly at depths of 24-27 m. After analyzing the causes of abnormal temperature, it was pointed out that there may be leakage points in this range. Subsequent excavation verifies that there is a water seepage point in the water stop curtain of the foundation pit. The water leakage point is located at the excavation depth of 25 m of the foundation pit, as shown in Figure 3.  
In order to monitor the change of groundwater level near the seepage point in real time and verify the reasonableness of the calculation and analysis results, groundwater level monitoring points (DSW01-DSW05) were set near the pre-detected seepage point  
Figure 3: Field test results of fiber optic temperature measurement system.  
Figure 2: The layout of the fiber optic field.
## 3 The Image-Based Model
### Time-Driven Layout  
Given a single user \(u\), we can encode a spatial attribute \(a\) as a stream of encoded colors over time. Figure 3A shows a sample locomotion (\(\{L_{0},L_{1},...,L_{t}\}\)) recorded over a specific amount of time inside a session volume. These locations can be progressively translated into colors, by applying Definition 2 to produce a signal. Each pixel of the stream refers to a well-defined 3D location in \(V\) (voxel) at a given time, provided the analyst (client tool) knows the position and extents of the session volume. Among several advantages including offline data manipulation and direct, lightweight GPU routines (see [27]), such layout offers optimal compression ratios when the PNG format is adopted to store the data. This result is obtained thanks to smooth variations of neighboring pixels, as they will be likely continuous RGB values [23] (for instance locomotion data--see experimental results Section 5).  
Multiple streams (users) can be vertically arranged into _time \(\times\) users_ image atlases called quantized session atlas (QSA) (see Figure 3B). For a given attribute \(a\), a session volume \(V\) is thus capable of encoding \(S_{a}(u,t)\) entirely in a single compact image and easily exchanged over the network. The QSA layout also allow interactive manipulation using 2D image operations, using offline processing or directly performed on GPU hardware. A QSA regarding a specific spatial attribute for instance, can be easily reduced by compressing the image along x-axis (time) using a nearest-neighbor resampling algorithm, to obtain a lossy but coherent data reduction, creating an approximation of the original record (see Figure 3C).
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 3 Numerical Tests and Results  
For microseismic events, it is a common practice to segment long-term monitoring records into shorter intervals. These segmented records may contain individual or multiple microseismic events. If there is no clear correlation between multiple events, they can be separated into individual microseismic records for further feature extraction and analysis. However, in practical applications, the presence of strong coherence between events, coupled with location offsets or delays caused by measurement or localization errors, can result in signal cross-interference and introduce crosstalk noise. Furthermore, multiple microseismic events may share underground structures and other complex factors, leading to intricate and unpredictable interaction effects among them. As a result, these events become challenging to separate.  
To effectively address these scenarios, this paper uses the separability of microseismic events as a foundation. It involves simulating the training of neural networks using both single-event microseismic records and multi-event microseismic records as feature datasets. This approach ensures that even when dealing with heavily coupled and inseparable multiple microseismic signals, the trained model can efficiently and accurately perform velocity modeling.