esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimising Amber Processing Using 3D Scanning: New Perspectives in Cultural Heritage
## 1 Introduction  
Gemstones and decorative minerals have been used in jewellery, adornments and decorations for centuries, and they hold economic value as well as a significant cultural value. Since ancient times, the processing of stone has been held in high esteem in the hierarchy of craftsmanship, and its underlying knowledge has been shrouded in mystery. Skills such as metalworking have always commanded admiration and respect for this profession [1] and can even be considered as intangible cultural heritage [2]. Therefore, it is crucial to possess knowledge of the properties of the materials being processed, as well as the tools and technologies used in the materials' transformation process.  
Materials used in the production of jewellery should possess certain properties such as high hardness, transparency, captivating colour and lustre [3]. This applies to both minerals such as corundum (ruby, sapphire), beryl (emerald, bixbitte), necessitates (garnet, zircon) as well as organic materials such as amber, jet, or pearl. These features determine the choice of processing methods. In view of the unique properties of each specific piece, the processing of gemstones is complex and expensive due to the absence of mass or serial production methods.  
One common challenge in gemstone processing is the susceptibility to mistakes. Typically, each processed stone has an irregular, unique shape, dimensional measurements, and an internal structure. Processing errors are irreversible and often cannot be rectified orrepaired [4]. Their characteristics and geometric properties are achieved through various processes, including cleaving, cutting, grinding and polishing [5; 6].  
The implementation of new technologies that increase efficiency in the manufacturing process and offer products that meet the expectations of a wide range of customers is necessary. The adoption of new technologies in gemstone processing, such as 3D scanning, is also driven by consumers who expect unique and personalised products. Jewellery serves as an example of such products, where the most valued items are unique and not mass-produced [7]. Meeting such demands requires significant effort and financial costs. The labour-intensive process involves designing and constructing models for the creation of moulds and patterns, and making revisions to the design is often impractical. However, with the advancement of computer-aided design and scanning devices, a breakthrough has occurred, enabling the use of tools that were once only within the realm of designers' dreams. Three-dimensional scanning technology allows for the acquisition of virtual models that reflect the shapes of gemstones, allowing work with models in virtual space [8; 9]. The use of virtual gemstone and jewellery models reduces costs (financial investments) in producing a particular item by minimising material losses during the grinding process [10].
## 5 Experiments  
We comparatively evaluated the discrimination accuracy and computational demand of the methods detailed in Section 4 on a set of supervised image classification tests using the datasets described in Section 3. In the remainder 'Experiment #\(N\)' will indicate the experiment ran on the colour texture images of Group #\(N\). Following the same approach as in recent related works [1; 14; 17; 18], we used the nearest-neighbour classification strategy (with \(L_{1}\) distance) in all the experiments.  
Performance evaluation was based on split-sample validation with stratified sampling. The fraction of samples of each class used to train the classifier was \(1/2\) for Experiments #1 and #2 and \(1/8\) for all the other experiments. In the first two cases, the choice was dictated by the low number of samples available (as few as four per class in some datasets); in the others, we opted for a lower training ratio in order to better estimate the robustness of the methods to the intra-class variability. The figure of merit (' accuracy\" in the remainder) was the ratio between the number of samples of the test set classified correctly and the total number of samples of the test set. For a stable estimation, the value was averaged over 100 different subdivisions into a training and test set.  
For each experiment, a ranking of method was obtained by comparing all the image descriptors pairwise and respectively assigning \(+1\), \(-1\) or \(0\) each time a method was significantly better, worse or not significantly different from the other. Statistical significance (\(\alpha=0.05\)) was assessed through the Wilcoxon-Mann-Whitney rank sum test [97] over the accuracy values resulting from the 100 splits.
## 4 Experimental Results
### Parametric Measures  
In this section, four indicators will be introduced to evaluate the performance of the experimental results. These indicators include the accuracy, recall, precision, and F1-score and are often used for measuring classification tasks, and can be mathematically defined as follows:  
\[\begin{array}{c}\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text {TN}+\text{FP}+\text{FN}}\\ \text{Recall}=\text{TP}/(\text{TP}+\text{FN})\\ \text{Precision}=\text{TP}/(\text{TP}+\text{FP})\\ \text{F1-score}=(2\,\times\,\text{Precision}\,\times\,\text{Recall})/(\text{ Precision}\,+\,\text{Recall})\end{array} \tag{4}\]  
where TP, TN, FP, and FN denote the true positive, true negative, false positive, and false negative, respectively. Accuracy can be regarded as the rate of correct prediction among all of the tested samples. Recall can be defined as the correct prediction rate of positive samples among the labelled positive samples, and precision is the correct prediction rate of the true positive samples among the predicted positive samples. Furthermore, the F1-score is reported herein to harmonize the performance between recall and precision, where value of 1 indicates highest performance and the worst score is 0.  
Figure 4: The examples of the NEU dataset, where Cr, In, Pa, PS, RS, and Sc denote the crazing, inclusion, patches, pitted surface, rolled-in scale, and scratches defects, respectively.  
Figure 5: \(Cont.\)
# Computational Modeling of U-Shaped Seismic Dampers for Structural Damage Mitigation
## 4 Simulation Results of Damper Behavior
### Local Phenomena: Strain, Stress, and Strain Energy  
The computational results of stress fields, considering the maximum displacement applied in the load protocol, identify the highest stress levels between the curved and arms zones of the damper (Figure 5a). These results indicate that the head of the damper, where the bores are located, despite displaying mid-to-high stress levels, does not significantly impact the geometry, as it remains within its elastic zone. Figure 5b shows that the AEPS of the damper, with a maximum value of 93%, is also concentrated in a very localized part of the damper between the curve and the arms parts. The maximum equivalent stress of 476 MPa is lower than the ultimate tensile stress of the material, which is correlated with the maximum strain value of 5.5% (Figure 5c). When analyzing the reference bilinear curve of effective stress-strain values, the von Mises equivalent values shown in Figure 5a are coincident with the local strain values of Figure 5c. Additionally, the strain energy fields depicted in Figure 5d show the maximum values located in very small zones where the maximum stresses occur. This behavior is confirmed by other research, with maximum localized deformation in the damping section [15; 26; 38], while the trends in the through-thickness have only been found in [26].  
A summary of the results of the local maximum values of the von Mises equivalent stress, strain, and energy for the maximum displacement of the 20-step load protocol is shown in Table 3. The maximum stress level is adequately located between the initial yield stress and the ultimate strength, confirming that the damper is working in the ductile plastic zone. This plastic deformation allows the device to dissipate energy transferred from the corresponding structure. Note that the AEPS is considerably greater than the equivalent strain because of the 20-step load; this is expected, as in each cycle, the AEPS increases with monotonic plastic strain. The strain energy shown in Figure 5d indicates the energy absorbed by the dampers, which is not stored after the recovery of elastic strain.
# Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention
## 3 Materials and Methods
### Experiment Configuration
#### 3.4.2 Experiment Design  
In this study, the experimental design plays a critical role in verifying the effectiveness of the proposed model. The research commences with a widely-recognized large dialogue dataset, from which data are randomly sampled and divided into three parts: 70% for training the model, which serve as the foundation for the model's learning; 15% for model validation, which are used to adjust hyperparameters and prevent overfitting during the model development process; and the remaining 15% for the test set, which are utilized to assess the model's final performance. This division strategy is intended to ensure that the model can make reasonable predictions on unseen data, thereby verifying the model's generalization capability.  
Five different baseline models were selected for comparison in the experiments: original transformer model, the traditional trusted execution environment (TEE) model, SOTER as a secure multi-party computation framework, the differential privacy (DP) model for measuring the strength of privacy protection, and a baseline model based on FHE. These models were chosen as baselines because they each represent different directions and the latest advancements in privacy protection technology. TEE provides an isolated execution environment but relies on the security of the hardware; SOTER protects the security ofdata in use through multi-party computation but may introduce additional communication overhead; DP is a commonly used technology in current privacy protection, it protects personal information by adding noise, but may sacrifice the utility of the data in some cases; the FHE model offers a powerful way to keep data encrypted while in use but comes with high computational costs. The comparison with these models allows for the demonstration of the relative advantages of the proposed model in terms of privacy protection and performance.  
The choice of optimizer is crucial for the model's convergence speed and final performance during the training process. The Adam optimizer was selected because it combines the benefits of momentum and adaptive learning rates, enabling the model to automatically adjust the learning rate at different stages of training, thus accelerating the convergence process and enhancing the stability of the model. After a series of preliminary experiments and parameter tuning, it was found that setting the learning rate to \(10^{-4}\), batch size to 32, and weight decay to \(10^{-5}\) allowed the model to achieve optimal performance. The selection of these hyperparameters strikes a balance between training speed and convergence quality, ensuring that the model can learn effectively at different stages.
###### Abstract  
Downhole drilling tool vibration measurement is crucial for drilling exploration safety, so real-time monitoring of vibration data is required. In this research, a honeycomb triboelectric nanogenerator (H-TENG) capable of adapting to various downhole environments is proposed. It can measure the frequency of downhole drilling equipment's vibrations and transfer mechanical energy to electrical energy for use in powering other low power downhole meters. In order to preliminarily verify the possibility of sensors used for vibration measurement of downhole drilling tools, we built a simulated vibration platform to test the sensing performance and vibration energy collection performance of H-TENG. According to the testing results, the measurement range of vibration frequency and amplitude are 0 to 11 Hz and 5 to 25 mm, respectively, and the corresponding measurement errors are less than 5% and 6%, respectively. For vibrational energy harvesting, when four sensors are wired in series with a 10\({}^{7}\) resistance, the maximum power is approximately 1.57 uW. Compared to typical methods for measuring downhole vibration, the honeycomb triboelectric nanogenerator does not need an external power source, it has greater reliability and output power, and it can vary its shape to adapt to the complicated downhole environment. In addition, the H-TENG can be combined freely according to the diameter of the drill string, and even if one sensor unit is damaged, the other units can still be used normally.  
triboelectric nanogenerator; vibration sensor; self-powered; downhole drilling tools; deformable +
Footnote †: journal: Applied Sciences  
1
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 2 Operational Risk Parameters for Urban Dense Cable Corridors
### Cable Body Risk Parameters
#### 2.1.4 Mechanical Damage  
Construction or other external forces may cause mechanical damage to the exterior of the cable, resulting in insulation damage or cable breakage. Additionally, the intrusion of rodents such as rats and mice can lead to mechanical damage by gnawing through the cable sheath, connectors, and terminals, potentially causing short circuits or exposure of live conductors.
## 3 Results
### Optimized Hyper-Parameter  
Through our three optimization methods, we obtained optimized hyper-parameters for each of our models, as shown in Table 2. The three optimization methods for SVM were configured with the same hyper-parameter, indicating that they would be ideal for SVM applications to this type of dataset. In contrast, both RF and XGBoost showed different hyper-parameters. For the RF models, Grid Search optimization generated a \"True\" value across all the imputation techniques, while Bayesian Search generated a \"False\" value for their bootstrap parameter. The XGBoost models were similar in that in their Grid Search optimization, the learning rates were more than 0.1, while in Random Search, the XGBoost models had a learning rate of 0.01 across all the imputation techniques. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Model**} & \multicolumn{3}{c}{**Optimized Parameters**} \\ \hline
**Algorithm** & **Imputation** & **Grid Search** & **Random Search** & **Bayesian Search** \\ \hline \multirow{8}{*}{SVM} & Mean & C = 1; gamma = 0.01; & C = 1; gamma = 0.01; & C = 1; gamma = 0.01; & kernel = RBF \\  & MICE & C = 1; gamma = 0.01; & C = 1; gamma = 0.01; & C = 1; gamma = 0.01; \\  & kernel = RBF & kernel = RBF & kernel = RBF \\ \cline{1-1}  & kNN & C = 1; gamma = auto; & C = 1; gamma = auto; & C = 1; gamma = auto; \\ \cline{1-1}  & RF & C = 1; gamma = 0.01; & C = 1; gamma = 0.01; & C = 1; gamma = 0.01; \\ \cline{1-1}  & RF & kernel = RBF & kernel = RBF & kernel = RBF \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimized hyper-parameters with the Grid, Random, and Bayesian Searches. Figure 9: Comparison of the average AUC score after 10-fold cross-validation. Furthermore, we calculated the processing time of each model for each optimization method for comparison. In this study, we used a free version of Google Colab with an Intel Xeon CPU (Santa Clara, CA, USA) with two virtual CPUs and 13 GB of RAM as its default CPU that runs on a laptop computer with an Intel i5 processor, 8 GB of RAM, and
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 1 Introduction
### Related Work
#### Dash7  
DASH7 [10] is a wireless sensor and actuator full Open Systems Interconnection (OSI) stack protocol that operates in the 433-MHz, 868-MHz and 915-MHz unlicensed ISM band/SRD band. It originates from the ISO 18000-7 standard [11] for active RFID, intended by the U.S. Department of Defense for container inventory. DASH7 inherits from ISO/IEC 18000-7 the default parameters of the active air interface communication at 433 MHz, an asynchronous MAC and a presentation layer using highly structured data elements. Furthermore, DASH7 extends and defines the protocol stack from the physical layer up to the application layer.  
DASH7 aims at providing communication in the range of up to 2 km, low latency, mobility support, multi-layer battery life, AES 128-bit shared key encryption support and a data rate up to 167 kbit/s.  
In [12], a more detailed survey of different technologies, including 3 GPP LTE Rel-13, Nokia's narrow-band LTE-M, Neul/Huawei's narrow-band proposal, Sigfox, etc., is provided.
## 3 Experimental Tests  
The circuit of Figure 1 was used to experimentally verify the proposed method. The tests were conducted using a 600 V-30 A Infineon GaN HEMT.  
The test conditions were: V\({}_{\text{DS}}\) = 75 V, V\({}_{\text{GS}}\) = 5 V. The SC duration of 15 \(\upmu\)s and the low drain voltage were chosen to avoid high thermal stress. This choice does not affect the results obtained as our goal is to demonstrate the feasibility of compensation. For the experimental tests, we used the two-step compensation network with the R and L values  
Figure 8: Simulated circuit with two steps compensation network.  
Figure 7: Simulated drain voltage (red) and drain current waveforms. I\({}_{\text{shcomp}}\) curve (green) represents the estimated current using compensation network, blue curve I(d) the actual current flowing into the device.  
(see Figure 8) coincident with those obtained from the LTSpice simulation. As can be seen from Figure 9, which shows a detail of the experimental circuit, to accurately set the inductance Ls1, which is 247 nH, we used a tunable inductor in the range 160-300 nH. The use of a variable inductor also allows us to compensate for any changes in the parameters related to the processes of realization of the devices and circuits that are not taken into account by the Spice models [14].  
Finally, Figure 10 shows the V\({}_{\text{DS}}\) voltage and the current evaluated in the two cases. The I\({}_{\text{D}}\) current obtained by direct measurement of the voltage across the shunt resistors is shown in green. The initial value of the current is about 80 A and then starts to decrease to reach about 9 A after 15 \(\upmu\)s. In blue we have the current measured using the proposed two-step compensation network. As can be seen, the measured current starts from at 17 A a considerably lower value than the previous one and then settles on 9 A.  
Figure 10: Experimental currents evaluated at V\({}_{\text{DS}}\) = 75 V and V\({}_{\text{GS}}\) = 5 V with (blue) and without (green) compensation network. The inset shows a zoom of the first microsecond of the transient.  
Figure 9: Detail of experimental circuit for SC tests with compensation network.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 3 Types of LiDARs
### Doppler (Wind) LiDAR  
The Doppler LiDAR leverages the Doppler effect to measure atmospheric wind speed by detecting frequency shifts in the backscattered signal caused by moving particles. Two primary approaches are used to determine principles for calculating the Doppler shift: direct detection (D-DWL) and coherent detection (C-DWL) [95].  
D-DWL directly measures the changes in signal intensity or photon counts of the backscattered signal. On the other hand, C-DWL detects both the intensity and the phase and frequency of the backscattered signal [95; 96]. C-DWL systems operate by transmitting pulses from a master laser; the backscattered signal--Doppler-shifted by atmospheric motion--is then mixed with a reference signal from the same laser source. This interference produces a beat frequency, which is analyzed to precisely determine the Doppler shift [96]. Compared to D-DWL, coherent detection systems provide higher measurement precision under equivalent signal-to-noise ratio (SNR) conditions, owing to their enhanced capability to resolve narrowband aerosol and cloud backscatter. Additionally, C-DWL systems generally provide superior spatial resolution for wind profilin [95; 97].  
Doppler LiDARs are widely used in atmospheric research and meteorology to obtain accurate, high-resolution wind measurements of wind profiles. Their application includes tracking weather patterns, assessing air quality, and improving numerical weather prediction models [98].
## 2 Four Channels Generator  
The basic structure of the four channels generator is presented in Figure 1, where four main blocks are identified: (1) the four positive Marx modulators, MM, also, identified as output channels, respectively, 0, 1, 2 and 3, each one operating independently; (2) capacitor charging power supply, CCPS, common to all channels, with voltage set by the PC user interface, U\({}_{\text{dc}}\); (3) main control board and trigger signals generator board, which receive the command lines from the PC user interface, programs all the four Marx modulators and sends the trigger signals to all the MM; (4) PC user interface, from where the user sets which channels produce pulses, the pulse sequence for each channel, and the amplitude voltage for each pulse in the sequence. The start pulses command can be internal or external, from an external trigger signal.  
The pulse generator operates in burst mode for each channel, with the possibility to repeat the burst sequence, from 2 to continuous operation, at a desirable frequency.  
At the output of each channel, voltage, and current monitoring are available, respectively, from a voltage divider with 200 gain and from a current transformer with 50 gain. These are helpful for diagnostic, depending on user applications.