esa-sceva/satcom-chunk-collection huggingface dataset:
# Erosion Rate Measurements for DART Spacecraft Ion Propulsion System +
## 3 Results
### Absolute Density Model Based on Near-Field LIF and Far-Field QCM Data
At larger forward angles, ion flux wins out over only deposition, and the QCM records net erosion. In the backflow region, the grid mask eliminates line of sight from the grid, but the QCM still detects low level deposition. The signal may be due to only ions whose trajectory is bent by local plume electric fields into the backflow region [24]. Figure 16 plots absolute moby sputter densities near the grid face. A simple linear fit was performed, without theoretical justification. \[\rho_{rel}\big{(}V_{x},V_{y}\big{)}=\frac{1}{2\pi\sigma^{2}}e^{-\frac{(|V_{x}-y_{ x}|^{2}+(V_{y}-y_{y})^{2})}{2\sigma^{2}}} \tag{9}\]4 Absolute molybdenum density near the grid face for DTL28-H-H.  
\[\rho_{rel}\big{(}V_{x},V_{y}\big{)}=\frac{1}{2\pi\sigma^{2}}e^{-\frac{(|V_{x}-y_{ x}|^{2}+(V_{y}-y_{y})^{2})}{2\sigma^{2}}} \tag{9}\]5 Sputter flux profile along 1-m arc for DTL28-H-H operating condition, based on near-field LIF measurement correlated with QCM data for absolute flux determination. Although the QCM data provide absolute deposition rate figures, the angular profiles generated typically involve significant uncertainty. This is because of several factors, including the complexities of slow deposition in a vacuum environment with low levels of residual gases and sensitivity to the local thermal environment because of imperfect sense and reference crystal matching.
# UAV Image Small Object Detection Based on RSAD Algorithm
## 1 Introduction
[20] proposed an MFT structure, which gives weights to different feature layers for fusion through the attention mechanism, solving the problem that different feature layers have the same influence on the results when fused, but the specific information in different feature layers is not considered for UAV image detection, and the improvement of the detection accuracy of UAV images is limited. [PERSON] et al. [21] proposed a dual neural network review method, by classifying the secondary features of the suspected object area, quickly screening the missed objects in the primary detection, extracting the feature map of the UAV image by using the VGG backbone, and combining the feature map and the location information of the suspected area for the secondary identification to realize the high-quality detection of the small objects in the UAV image; however, this structure increases the complexity of the model and makes the detection speed to decrease. The object detection algorithm of UAV image at the present stage improves the detection accuracy of UAV image by inefficient feature fusion and increasing the attention mechanism, but the detection speed is slow, and the practicality is poor. In this study, a RSAD algorithm for object detection in UAV images is proposed from SSD, and a feature fusion module SAFM with self-attention mechanism is designed, through which information from different layers is efficiently fused, and residual learning and Focal Loss loss function are utilized to improve the detection accuracy of UAV images with less loss in detection speed.
## 3 Results and Discussion
### External Sampling Cell Efficiency and Ammonium Monitoring
Specifically, the bioreactor operated at a mesophilic range of \(37\)\({}^{\circ}\)C, and the sample remained in the measurement cell for a few minutes, reaching a temperature of around \(32\)\({}^{\circ}\)C. However, the instrument calibration was conducted at approximately \(20\)\({}^{\circ}\)C, as ammonia ion concentration decreases with increasing temperature, assuming constant pH values [54]. Performing calibration at temperatures closer to the bioreactor's conditions requires specialized equipment, making it challenging to implement. Under these conditions, a potential solution to the above issues could be to perform dilutions [55] within the measurement cell, initially taking a smaller sample volume from the bioreactor. This approach would bring the temperature closer to the calibration environment while reducing the solid content in the final diluted solution for ammonium concentration measurement, mitigating the membrane clogging issue.
## 4 Discussion
### Strengths and Limitations  
In the previous studies, smoking data were mainly collected through two sources: a structured questionnaire or a manual review of the clinical notes by researchers. Using a structured questionnaire may require a lot of effort to increase the response rate. Similarly, manual review needs significant amount of time and human resources with the possibility of human error. Our proposed method allows researchers and practitioners to easily obtain smoking information from EHR. It is also the first work to extract smoking information from the clinical free text that contains two different languages, Korean and English. Previous works on smoking status classification focused on binary classification. Their algorithms classified either smokers and non-smokers or past smokers and non-smokers. However, our classification algorithm based on keyword extraction uses multilabel classification that distinguishes between the current, past, non-smokers, and unknowns. Various treatment guidelines for noncommunicable diseases [51; 52] recommend examining smoking history during every outpatient visit and educating lifestyle modification. If there is no smoking-related information in the patient's previous EHR, the medical staff can perceive it as a cue to collect new information. Moreover, it was confirmed that smoking-related keywords were expressed in various ways even if the previous chart contents were copied and pasted. Our proposed algorithm will have a practical application in automatically mapping and preprocessing unstructured clinical notes composed of various keywords that occur under the copy and paste practice.  
One limitation of this study is the possibility that some clinicians did receive patients' smoking history but failed to enter it into EHR. Therefore, simply labeling patients with unknown smoking status as the unknowns may be insufficient. Second, our study was conducted only with free-text clinical notes obtained from two departments in one tertiary hospital. The clinical notes of diabetes patients in the family medicine and COPD patients in the respiratory medicine often include the patients' smoking history, allowing us to build and validate our approach. Further validation study using data from other centers is required to test our proposed method's robustness and applicability.
## 3 Results  
Table 1 shows the means of active wrist ROM movements measured from a standard goniometer, a smartphone device, and the proposed IMU glove for the flexion (x), extension (x), supination (y), pronation (y), ulnar deviation (z), and radial deviation (z). Average measurements from the three devices had within 0.5 degrees of differences from each other for flexion, ulnar deviation, and pronation. Extension and supination motions had within 1 degree of difference, whereas radial deviation had within 1.5 degrees of differences from each other.  
Table 2 shows the ICC for a smartphone device and the proposed IMU glove with a standard goniometer as the gold standard. There are strong correlations between the IMU glove and a standard goniometer for all the wrist ROM measurements with the ICCs between 0.914 and 0.961 (\(p\)-values \(<\) 0.001). For a smartphone device, except for the wrist supination with the ICC of 0.828 (\(p\)-value \(<\) 0.001), other wrist ROM measurements had strong correlations with a standard goniometer. To evaluate the operator bias, Table 3 shows the ICC for the three operators who performed the experiment. Based on the 95% confidence interval of the ICC estimation, all of the movements had strong reliability across the three devices; the ICC was greater than 0.9 with the \(p\)-value of less than 0.001.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Goniometer** & **Smartphone** & **IMU Glove** \\ \hline Flexion (x) & 87.57 \(\pm\) 2.42 & 87.42 \(\pm\) 3.34 & 87.92 \(\pm\) 2.54 \\ Extension (x) & 85.02 \(\pm\) 4.12 & 85.22 \(\pm\) 4.00 & 86.00 \(\pm\) 3.69 \\ Supination (y) & 23.47 \(\pm\) 2.18 & 23.70 \(\pm\) 1.83 & 24.20 \(\pm\) 2.08 \\ Pronation (y) & 27.70 \(\pm\) 1.72 & 27.25 \(\pm\) 1.85 & 27.59 \(\pm\) 1.48 \\ Ulnar deviation (z) & 86.23 \(\pm\) 2.30 & 86.28 \(\pm\) 2.69 & 86.73 \(\pm\) 2.40 \\ Radial deviation (z) & 88.27 \(\pm\) 2.40 & 86.97 \(\pm\) 3.38 & 87.65 \(\pm\) 2.87 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The means of active wrist range of motion movements measured with the three devices.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **ICC** & \multicolumn{3}{c}{**95\% Confidence Interval**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{3-4}  & & **Lower** & & \\ \hline
**Flexion (x)** & & & & \\ Smartphone & 0.929 & 0.820 & 0.972 & \textless{}0.001 \\ IMU Glove & 0.955 & 0.887 & 0.982 & \textless{}0.001 \\
**Extension (x)** & & & & \\ Smartphone & 0.951 & 0.877 & 0.981 & \textless{}0.001 \\ IMU Glove & 0.921 & 0.801 & 0.969 & \textless{}0.001 \\
**Supination (y)** & & & & \\ Smartphone & 0.828 & 0.565 & 0.932 & \textless{}0.001 \\ IMU Glove & 0.933 & 0.832 & 0.974 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Intraclass correlation coefficient of a smartphone device and the proposed IMU glove with standard goniometer as the gold standard.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 2 Related Work  
### Environmental Factors and Mental Health  
Environmental factors play a significant role in influencing mental health outcomes. Various studies have highlighted the impact of physical surroundings, such as urban environments, pollution, noise, and climate change, on psychological well-being. Urbanisation, for instance, has been linked to increased stress levels and higher risks of anxiety and depression due to factors like overcrowding, lack of green spaces, and heightened exposure to noise pollution [11; 12]. Air pollution, particularly exposure to fine particulate matter (PM2.5), has been associated with cognitive decline and the exacerbation of psychiatric disorders [13]. Similarly, natural disasters and extreme weather events, which are intensifying due to climate change, have been shown to contribute to post-traumatic stress disorder (PTSD), depression, and anxiety disorders [14].  
Understanding these relationships underscores the need for interdisciplinary approaches combining public health, environmental science, and urban planning to address the mental health challenges associated with environmental factors.
## 6 Results
### Compressive Strength
Hence, SF can enhance the bond of binder matrix with nylon fibers, and mixed with silica fume exhibits more gain in compressive strength as compared to mixes with no silica fume. A study of bonds performed by [53] revealed that utilizing SCMs such as fly ash can improve the ITZ of binder matrix with the fibers, which need more increased effort to pull out strands from the cement. This assisted in improving cement-based composite's mechanical strength. Similar to fly ash, the potential of C-H consumption and the filler effect of silica fume could also reinforce the ITZ between binder matrix and nylon fibers. A relative analysis of compressive strength tests is shown in Figure 2. A relative analysis is performed on concrete samples to show that how much its performance is improved concerning the control sample. It was observed that using nylon fiber and silica fume, RCA level can be exploited to obtain comparable or better performance as compared to the reference sample. For example, RCA50-SF20 and RCA50-SF20-NFRC exhibit similar and improved performance as compared to the reference sample at 28 days of curing. Likewise, at 90 days of curing RCA100-SF20 and RCA100-SF20-NFRC display improved performance as compared to the reference sample at 28 days of curing. It can be concluded that the joint utilization of silica fume, nylon fiber can assist to enhance the RAC performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**7 Days**} & \multicolumn{2}{c}{**28 Days**} & \multicolumn{2}{c}{**90 Days**} \\ \cline{2-7}
**Mix ID** & **Standard** & **Coefficient of** & **Standard** & **Coefficient of** & **Standard** & **Coefficient of** \\  & **Deviation** & **Variance** & **Deviation** & **Variance** & **Deviation** & **Variance** \\ \hline RCA0-SF0-PC & 0.25 & 2.26 & 0.550 & 4.734 & 0.321 & 2.649 \\ RCA50-SF0-PC & 0.35 & 2.86 & 0.680 & 5.641 & 0.971 & 7.853 \\ RCA100-SF0-PC & 0.92 & 5.93 & 0.888 & 7.226 & 0.655 & 4.342 \\ RCA0-SF20-PC & 0.4 & 2.35 & 0.986 & 5.826 & 0.568 & 3.261 \\ RCA50-SF20-PC & 0.95 & 9.63 & 0.404 & 2.618 & 0.862 & 8.425 \\ RCA100-SF20-PC & 0.4 & 3.52 & 0.763 & 7.983 & 0.5 & 4 \\ RCA0-SF0-NFRC & 0.3 & 2.38 & 0.850 & 7.310 & 0.529 & 3.948 \\ RCA50-SF0-NFRC & 0.96 & 6.02 & 0.611 & 4.037 & 0.5 & 3.448 \\ RCA100-SF0-NFRC & 0.65 & 3.74 & 0.251 & 1.4085 & 0.458 & 2.618 \\ RCA0-SF20-NFRC & 0.86 & 8.42 & 0.754 & 6.801 & 0.346 & 3.207 \\ RCA50-SF20-NFRC & 0.75 & 6.74 & 0.854 & 8.993 & 0.763 & 5.951 \\ RCA100-SF20-NFRC & 0.63 & 6.93 & 0.650 & 5.437 & 0.680 & 4.944 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Standard Deviation and Coefficient of Variation for Compressive Strength (MPa).
## 4 Results
### Pore Types  
Using [PERSON]'s (1970) [55] classification, four pore types were identified in the studied samples (Figure 5): inter-particle, intra-particle, fracture, and moldic pores. In addition, we used the [PERSON] [58] definition of microporosity (pores that have a size of 10 micron or less) to describe and quantify the microporosity. Microporosity is the most common pore type, as dissolution pores (moldic and vugs) are not frequent. Other pore types such as interparticle pores are only found in grain-dominated samples (Figure 5A). Moreover, dissolution-related pores (molds) are very rare in the studied samples, with only two samples showing moldic pores resulting from the dissolution of skeletal grains such as dasycladacean algae. Only one sample displays vugs as the dominant pore type, which are partially filled with coarse-grained sediments, e.g., ooids and peloids (Figure 5D).  
Figure 4: Quartz-rich peloidal packstone with some skeletal fragments (yellow arrows). Quartz grains range in size between 30 to 200 microns. Scale bar at bottom right.  
The bioturbation intensity (BI), which is the total volume of the sample that is occupied by burrows or trace fossils, in the studied samples was measured using a CT scan (see Section 3.7). The bioturbation intensity shows a very weak correlation, R2 < 0.2, with both porosity and permeability (Figure 8A,B). However, the burrow filling material seems to have a significant control on porosity and permeability values, with the coarse-filled burrows having higher values (\(\mu\) = 14.44% and 0.56 mD) than fine-filled burrows (\(\mu\) = 6.56% and 0.07 mD) (Figure 8C,D).  
Figure 6: _Thalassioides_ burrows of the Hanifa Formation. Outcrop photos showing burrows filled with coarser materials than matrix (**A**), or partially to completely unfilled burrows (**B**). Pen (**A**) and hammer for scale (**B**). Scan of two of the studied samples showing burrows filled with (**C**) fine material in grain-dominated matrix highlighted by red dotted lines, and (**D**) coarse fill in mud-dominated matrix. Scale bar in cm in (**C,D**).
# Physics-Based Data Augmentation Enables Accurate Machine Learning Prediction of Melt Pool Geometry
## 2 Materials and Methods  
### Experimental Materials and Design  
The material used in this study was atomized 316 L stainless steel powder provided by BLT Corporation, whose detailed chemical composition is listed in Table 1. The powder exhibits high sphericity, excellent flowability, and uniform spreading, making it suitable for laser powder bed fusion (L-PBF) processing. To remove surface moisture, the powder was sieved using a 250-mesh standard sieve and dried in a vacuum oven at 80 \({}^{\circ}\)C for 4 h prior to use.  
L-PBF fabrication was conducted on an HBD-80 machine equipped with a Yb fiber laser (wavelength 1040 nm, maximum power 200 W), with an effective build size of \(\mathcal{O}\)120 mm \(\times\) 80 mm. Throughout the printing process, the build chamber was shielded with argon gas to maintain an oxygen concentration below 1000 ppm and suppress oxidation.  
To avoid unstable or low-energy regimes that lead to process defects, a total of 36 sets of process parameter combinations were systematically designed using different levels of laser power and scanning speed to cover a wide range of energy input conditions. The process parameters are shown in Table 2. As illustrated in Figure 1, under each process condition, 15 single-track melt pool samples were fabricated using L-PBF, and the cross-sectional dimensions were measured and averaged to minimize experimental uncertainties.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Elements** & **Fe** & **C** & **Cr** & **Ni** & **Mo** & **Mn** & **Si** \\ \hline
316 L & 67.98 & 0.0086 & 17.11 & 11.01 & 2.42 & 0.81 & 0.66 \\ \hline \end{tabular}
\end{table}
Table 1: The chemical composition of the 316 L powder (wt%).
# WTA: A Static Taint Analysis Framework for PHP Webshell
## 3 Motivation

Table 3: The sources of the webshells (Accessed on 7 January 2021). Besides, we selected eight currently popular webshell detection systems for the controlled experiment with WTA. They were D-Shield [15], ShellPub [34], WebshellKiller [35] (precision mode), WebshellKiller [35] (recall mode), CloudWalker [36], ClamAV [37], LoKi [38], and findbot.pl [39]. The basic information and basic detection principles of these tools are shown in Table 5.

### Effectiveness Test of the Interprocedural Analysis Module Based on Taint Analysis (RQ1)

To evaluate the effectiveness of the interprocedural analysis module, we implemented two versions of the webshell static detection tool: Webshell Taint Analysis (WTA) and No Interprocedural Analysis Module (WTA-NO-IAM). The former uses the method proposed in this paper, while the latter does not include the interprocedural analysis module. In this experiment, there were two performance evaluation indicators. First, validating sample code was set up to test the validity of two types, user-defined function-type webshell and include-type webshell. Second, WTA and WTA-NO-IAM were applied to detect our webshell dataset, and the effectiveness of the interprocedural analysis module was evaluated by comparison of the number of webshells detected by the two tools. The validating sample code for the webshell of the user-defined function-type webshell (WebShell-1) and the include-type webshell (WebShell-2) is shown in Listing 5. As shown in Listing (a), the sample webshell encapsulates the easy-to-detect keyword \"_POST\" in the user-defined function \"dynamic\" and the easy-to-detect dangerous function \"assert\" in the user-defined function newassert(), trying to bypass the detection of the webshell detection tool. Finally, at Line 16, they are concatenated into a one-sentence webshell: assert($_POST[' x ']).

The include-type webshell is shown in Lists (b) and (c), where List (b) is the body of the webshell and List (c) is the content of the included file. The sample webshell uses \"include\" to wrap the dangerous function \"eval\" into the user-defined function \"HelloWorld\" and calls \"HelloWorld\" in the body of the webshell file. The webshell is finally achieved at Line 3 of the list (b): eval ($_POST['hello']). WTA, WTA-NO-IAM, and famous webshell tools were used to detect the two webshells, and the test results are shown in Table 6.
## 3 Results
### Clustering
#### 3.2.2 Reproducible Results  
To compare the clustering performances with reproducible output, as described in the Methods section, from 100 clustering outputs, we computed a final and reproducible clustering result for each NN. Of these, we selected the one with an NN that maximizes  
Figure 4: Variability of clustering performances. Quantitative results of HDBSCAN (**a**), DBSCAN (**b**), OPTICS (**c**), and agglomerative clustering (**d**) applied to \(T=100\) UMAP embeddings, in terms of ARI for each NN. From left to right, panels correspond, respectively, to Euclidean, spherical, and hyperbolic space. Each boxplot represents the distribution of the ARI values, along the \(T\) iterations for each specific NN.  
the ARI. As shown in supplementary results (Tables A2 and A3), the best reproducible results are:  
* \(\mathrm{N}_{c}=4\) and ARI equal to **0.47** (spherical metric), \(\mathrm{N}_{c}=2\) and ARI equal to 0.33 (Euclidean metric), and \(\mathrm{N}_{c}=3\) and ARI equal to 0.46 (hyperbolic metric), for HDBSCAN;
* \(\mathrm{N}_{c}=3\) and ARI equal to 0.39 (spherical metric), \(\mathrm{N}_{c}=4\) and ARI equal to 0.45 (Euclidean metric), and \(\mathrm{N}_{c}=4\) and ARI equal to **0.46** (hyperbolic metric), for DBSCAN;
* \(\mathrm{N}_{c}=4\) and ARI equal to 0.28 (spherical metric), \(\mathrm{N}_{c}=4\) and ARI equal to **0.38** (Euclidean metric), and \(\mathrm{N}_{c}=4\) and ARI equal to **0.38** (hyperbolic metric), for agglomerative clustering;
* \(\mathrm{N}_{c}=3\) and ARI equal to 0.41 (spherical metric), \(\mathrm{N}_{c}=3\) and ARI equal to **0.44** (Euclidean metric), and \(\mathrm{N}_{c}=3\) and ARI equal to **0.44** (hyperbolic metric), for OPTICS.  
Even with our reproducible pipeline, in terms of ARI the Euclidean and hyperbolic metrics have identical performances, higher than the spherical one on three (DBSCAN, OPTICS, and agglomerative clustering) clustering algorithms out of four. Only with HDBSCAN does the Euclidean metric perform worse than spherical and hyperbolic metrics which have similar best ARI values: 0.47 and 0.46, respectively. Overall, we observed the best performance with spherical embedding coupled with HDBSCAN: an ARI index of 0.47, a homogeneity score of 0.43, and four clusters. However, as explained in the Reproducibility section, we cannot have a truthful latent space visualization. We therefore plotted our best final clustering labels on the more representative UMAP embedding (Figure 5b).
# Barrel Shifter Physical Unclonable Function Based Encryption
## 2 General Encryption Protocol  
Figure 1 shows our proposed PUF-based general encryption protocol which depicts [PERSON] as the sender and [PERSON] as the receiver. Both [PERSON] and [PERSON] have their own PUF. If [PERSON] encrypts his message \(m\) with his PUF as \(f_{[PERSON]}(m)\), [PERSON] has no way to decrypt it except to ask [PERSON] to decrypt it for her. The following protocol overcomes this asymmetry:  
1. [PERSON] encrypts the message \(m\) with \(f_{[PERSON]}\).
2. [PERSON] sends \(f_{[PERSON]}(m)\) to [PERSON].
3. [PERSON] encrypts \(f_{[PERSON]}(m)\) with \(f_{[PERSON]}\) (At this point, [PERSON] does not know the message \(m\)).
4. [PERSON] sends \(f_{[PERSON]}(f_{[PERSON]}(m))\) to [PERSON].
5. [PERSON] decrypts \(f_{[PERSON]}(f_{[PERSON]}(m))\) with \(f_{[PERSON]}^{-1}\) and obtains \(f_{[PERSON]}(m)\).
6. [PERSON] sends \(f_{[PERSON]}(m)\) to [PERSON].
7. [PERSON] decrypts \(f_{[PERSON]}(m)\) with \(f_{[PERSON]}^{-1}\) and obtains the message \(m\).  
Message confidentiality is maintained by entangling message bits with physical randomness. The entangling process must be both invertible and commutative so that: \(f_{[PERSON]}\) and \(f_{[PERSON]}^{-1}\) can cancel each other out; the order of \(f_{[PERSON]}\) and \(f_{[PERSON]}\) can be changed. The entangled message \(m^{\prime}\) is designed not to be linearly related with \(m\); this makes it hard for an eavesdropper to learn \(m\) by examining intermediate messages.