esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Isochoric Freezing on the Quality Characteristics of Raw Bovine Milk
## 2 Materials and Methods
### Volatile Organic Compounds  
Volatile organic compounds (VOCs) in the milk samples were measured using headspace solid-phase microextraction (HS-SPME) techniques. Sample preparation methods closely followed procedures described by [PERSON] et al. [13] with few modifications. Briefly, 10 mL of milk, 3.7 g of sodium chloride, 250 \(\upmu\)L of internal standard (aqueous cyclohexanone solution at 25 \(\upmu\)g/mL), and a magnetic stir bar were added to a 240 mL screw top amber vial and sealed with a PTFE/silicone septum (22 mm \(\times\) 2.54 mm thickness) and plastic screw cap combination. Sample vials were subsequently equilibrated in a water bath at 50 \({}^{\circ}\)C and gently stirred using a magnetic stir plate (80 rpm). Prior to extraction, the SPME fiber (DVB/CAR/PDMS, 50/30 \(\upmu\)m coating, 2 cm length) was heated in the injection port (250 \({}^{\circ}\)C) of an Agilent 7890B gas chromatograph for 15 min.  
After temperature equilibration of the sample vial, the SPME fiber was exposed to the headspace for 30 min while stirring. Fibers were then desorbed for 10 min (splitless) on a gas chromatograph (7890B) coupled to a mass selective detector (5977A MSD; Agilent Technologies, Palo Alto, CA, USA). The injector temperature was set at 250 \({}^{\circ}\)C. A 60 m \(\times\) 0.25 mm d\({}_{\mathrm{f}}\) = 0.25 \(\upmu\)m DB-1 fused silica capillary column was used, using helium as a carrier gas at a constant flow of 1 mL/min (99.999% purity). The GC oven was programmed with the following conditions: 35 \({}^{\circ}\)C (5 min), ramped at 4 \({}^{\circ}\)C/min until 100 \({}^{\circ}\)C, a second ramp at 10 \({}^{\circ}\)C/min until 220 \({}^{\circ}\)C, and finally held at 220 \({}^{\circ}\)C (5 min). The purge valve to split vent was opened after 1 min at a flowrate of 30 mL/min following sample injection. The total run time lasted approximately 45 min. The MS transfer line and ion source were maintained at 250 \({}^{\circ}\)C and 230 \({}^{\circ}\)C, respectively. The MS quadrupole temperature was set to 150 \({}^{\circ}\)C with an electron ionization of 70 eV, which was set to full scan mode (\(m/z\) 20 to 350 at 1.6 scan/s).  
MassHunter Unknowns Analysis software (v. B.07.01) was used for data processing using the deconvolution algorithm. Identification of the VOCs was based on a combination of computer matching with mass spectral libraries (NIST 14) and retention index values derived from an n-alkane solution (c5 to n-c18) (Sigma Aldrich Co.). Only compounds with match factor scores \(\geq\) 70 and signal-to-noise ratios \(\geq\) 10 were selected for quantitation. Semi-quantitation was performed using cyclohexanone as the internal standard (IS). Results are expressed in \(\upmu\)g eq. IS per 100 mL of milk.
## 4 Materials and Methods
### ChIP Sequencing  
ChIP assays and RNA immunoprecipitation were performed as previously reported [42]. NMuMG cells were crosslinked in 1% formaldehyde for 10 min at room temperature followed by the addition of glycine (125 mM final concentration) for 5 min. Nuclei were isolated using lysis buffer 1 (5 mM Pipes pH 8, 85 mM KCl, 0.5% NP-40, and complete protease inhibitor cocktail (Roche, Hoffmann-La Roche Inc., Nutley, NJ, USA)) and were lysed using lysis buffer 2 (1% SDS, 10 mM EDTA, 50 mM Tris-HCl pH 8.1, and complete protease inhibitor cocktail (Roche)). Chromatin was sheared into an average size of 500 bp by eight pulses of 30 s (30 s pause between pulses) at 4 \({}^{\circ}\)C in the water bath sonicator Bioruptor (Diagenode, Liege, Belgium).Thirty micrograms of chromatin were diluted 1:10 in IP buffer (0.01% SDS, 1.1% Triton X-100, 1.2 mM EDTA, 16.7 mM Tris-HCl pH 8.1, 167 mM NaCl, 1% sodium deoxycholate) and incubated overnight at 4 \({}^{\circ}\)C in rotation with 5 ug of monoclonal antibody anti-Lamin A/C (sc-376248; Santa Cruz) or anti-rabbit IgG control antibody (Sigma). Complexes were collected using Protein-A Dynabeads pre-blocked with BSA (New England Biolabs) and transfer RNA (Roche) and then washed with wash buffer 1 (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl pH 8.1, and 150 mM NaCl, 1% sodium deoxycholate), wash buffer 2 (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl pH 8.1, 500 mM NaCl, 1% sodium deoxycholate), wash buffer 3 (0.25 M LiCl, 1% NP-40, 1% sodium deoxycholate, 1 mM EDTA, 10 mM Tris-HCl pH 8.1), and twice with TE 1\(\times\) buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA). The complexes were eluted twice from the beads with elution buffer (1% SDS in TE 1\(\times\) buffer) by incubating 10 min at 65 \({}^{\circ}\)C. The eluates and the inputs were incubated overnight at 65 \({}^{\circ}\)C for decrosslinking and treated with Proteinase K for 1 h at 37 \({}^{\circ}\)C. Chipped DNA was purified with phenol: chloroform extraction followed by ethanol precipitation and resuspended in Milli-Q water. The sequencing was performed with the Illumina HiSeq000 system (Illumina) with 75 bp paired-end reads at the Genomic Unit of CABIMER (Sevilla, Spain). ChIP-seq reads for each condition were independently aligned against the MGSCv37/mm9 mouse reference genome using the align function from the Bioconductor Rsubread package (v1.28.1) with type = 1, TH1 = 2 and unique = TRUE parameters. Then a differential binding analysis was performed to identify Lamin Associated Domains (LADs) using the csaw (v1.12.0) and edgeR (v3.20.9) packages. Briefly, aligned reads were counted in bins of 25 kb along the genome using the windowCounts function and read coverage of each bin was normalized to library size using calcNormFactors. To perform differential binding analysis for each window using input as the control, a sequential number of functions were used: estimateDisp, glmFit and glmLRT. Finally, enriched windows were merged as they were overlapping or adjacent using the mergeWindows function.
## 2 Vertical Acceleration and Turbulence Severity Estimation
### Vertical Acceleration Computation in Turbulent Flight  
#### 2.3.1 Local Velocity Induced by Aircraft's Unsteady Motion  
The nonplanar and unsteady vortex ring method (VRM) is applied to study the aerodynamic characteristics in turbulent flight. Local velocity induced by aircraft's unsteady motion at arbitrary point can be divided by three parts. The first part is the far-field free flow \(\mathbf{V}_{\infty}(t_{k})=\left[V_{\text{o}x}(t_{k}),V_{\text{o}y}(t_{k}),V_{ \text{o}z}(t_{k})\right]^{T}\). The second part is the angular rate \(\left[p(t_{k}),q(t_{k}),r(t_{k})\right]^{T}\) effects of the aircraft. The third part is the instantaneous turbulence \(\mathbf{W}(t_{k})=\left[W_{x}(t_{k}),W_{y}(t_{k}),W_{z}(t_{k})\right]^{T}\) effects. The local velocity of arbitrary point at any time \(t_{k}\) can be described by Equation (4), with \(\left[x,y,z\right]^{T}\) the coordinate of arbitrary point,  
\[\left[\begin{array}{c}V_{x}(t_{k})\\ V_{y}(t_{k})\\ V_{z}(t_{k})\end{array}\right]=\left[\begin{array}{c}V_{\text{o}x}(t_{k}) \cos\alpha(t_{k})\cos\beta(t_{k})\\ -V_{\text{o}y}(t_{k})\sin\beta(t_{k})\\ V_{\text{o}z}(t_{k})\sin\alpha(t_{k})\cos\beta(t_{k})\end{array}\right]+ \left[\begin{array}{c}-q(t_{k})z+r(t_{k})y\\ -r(t_{k})x+p(t_{k})z\\ -p(t_{k})y+q(t_{k})x\end{array}\right]+\left[\begin{array}{c}W_{x}(t_{k})\\ W_{y}(t_{k})\\ W_{z}(t_{k})\end{array}\right] \tag{4}\]
# Li2O-Based Cathode Additives Enabling Prelithiation of Si Anodes
## 2 Materials and Methods
### Coin Cell Testing  
The 2032-type coin cells were fabricated in an Ar-filled glove box. Half cells consisted of 15.5 mm diameter Li metal (99.9%, 0.75 mm thick, Alfa Aesar), 19 mm diameter Celgard 2325 separator, 14 mm diameter cathode, and 40 \(\upmu\)L Gen2 electrolyte (1.2 M lithium hexafluorophosphate (LiPF\({}_{6}\)) in ethylene carbonate (EC)/ethyl methyl carbonate (EMC) (3.7, \(w/w\), Tomiyama Pure Chemical Industries, Ltd. (Tokyo, Japan)). Full cells consisted of 15 mm diameter Si anode, 19 mm diameter Celgard 2325 separator, 14 mm diameter cathode, and 40 \(\upmu\)L Gen2 electrolyte. The Si electrode was provided by the Cell Analysis, Modeling and Prototyping (CAMP) Facility at Argonne National Laboratory. The anode was a mixture of 80 wt % Si (Paraclete Energy), 10 wt % C45 conductive carbon (Timcal), and 10 wt % lithium polyacrylate binder (LiPAA) mixture coated on a 10 \(\upmu\)m thick copper (Cu) foil with a coating loading of 1.10 mg/cm\({}^{2}\) and thickness of 10 \(\upmu\)m. The electrode exhibited 2.85 mAh/cm\({}^{2}\) during the first lithiation in a half cell with a 50 mV vs. Li/Li\({}^{+}\) cutoff voltage. The Si electrode was dried at 150 \({}^{\circ}\)C under vacuum and the Celgard 2325 separator was dried under a vacuum at room temperature prior to use.  
Coin cells were cycled using a MACCOR Series 4000 Automated Test System. The cells were placed inside a MACCOR Model MTC-020 Temperature Chamber, which was set to 30 \({}^{\circ}\)C. A \(\sim\)1.9 mA current was applied, which corresponded to a 1C rate for all cells. The same charge and discharge rates were applied without resting in between.
## 4 Computing _Rmq_, _Psv_, and _Nsv_
### Experimental Comparison  
We tested the performance of the different _NPR_ implementations by performing 100,000 _NSV_ and _RMQ_ queries at random positions in the _LCP_ array. Figure 3 shows the space/time achieved for each implementation on dna and proteins (the others texts gave very similar results). We consider the four selected LCP representations, which affect the performance because the algorithms have to access the array. The space we plot is additional to that to store the _LCP_ array. We obtained space/time tradeoffs by using different block sizes \(L=8,16,32\). Note that _RMQ_ on _FMN-RMQ_ does not access _LCP_, so the value of \(L\) does not affect it.  
Figure 3: Space/Time for the operations _NSV_ and _RMQ_. Times for _PSV_ are identical to those for _NSV_.  
First, it can be seen that _RMM-NPR_ performs well using much less space than _FMN-NPR_. In addition, solving _NSV_ (and thus _PSV_) with _RMM-NPR_ structure is significantly faster than with _FMN-NPR_. This holds under all the _LCP_ representations, regardless that times are one order of magnitude higher with representations _Sad-Gon_ and _FMN-RRR_.  
On the other hand, _RMQ_ is way faster on _FMN_ because it does not need to access _LCP_ at all. It is 2-3 times faster than _RMM_ using _DAC_ or _DAC-Var_ and two orders of magnitude faster than _RMM_ using _Sad-Gon_ or _FMN-RRR_. This operation, however, is far less common than _NSV_ and _PSV_ when implementing the CST operations, so its impact is not so high. Another reason to prefer _RMM-NPR_ is that it can support a generalized version of _NSV_ and _PSV_, which allows us to solve operation _LAQ\({}_{T}\)_ on suffix trees without adding more memory, as opposed to _FMN-NPR_. In the next section we show how a fully-functional CST can be implemented on top of the _RMM-NPR_ representation.
# Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning
## 5 Evaluation
### Evaluation Metrics  
We evaluate the performance of our models using accuracy, precision, recall, F1 score, and memory usage as the performance indicators in this paper. Accuracy measures the overall correctness in classifying both normal and abnormal nodes. Precision assesses the ability to correctly identify botnet nodes, while recall measures the ability to detect all botnet attacks. The F1 score is the harmonic mean of precision and recall, providing an overall measure of the effectiveness of the detection models.  
These metrics are calculated using the following formulas:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{13}\]  
\[Precision=\frac{TP}{TP+FP} \tag{14}\]  
\[Recall=\frac{TP}{TP+FN} \tag{15}\]  
\[F_{1}=\frac{2 Precision\times Recall}{Precision+Recall} \tag{16}\]  
In the context of flow classification, _TP_ (True Positive) represents the number of flows correctly classified as positive (e.g., botnet attacks), _TN_ (True Negative) represents the number of flows correctly classified as negative (e.g., normal traffic), _FP_ (False Positive) refers to the number of flows incorrectly classified as positive, and _FN_ (False Negative) represents the number of flows incorrectly classified as negative.
## 2 Materials and Methods
### Data and Preprocessing of the Data
#### 2.2.3 Dataset Partition  
In classifying land covers and crop types, addressing imbalanced distributions is crucial as it can skew training data and affect classification accuracy. To mitigate this, we balanced class distributions by reducing samples in majority classes, then divided pixels into training, validation, and test sets in proportions of 60%, 20%, and 20%, respectively. The sets, totaling 1,074,097, 358,027, and 358,047 pixels, respectively, were used for model training, hyperparameter and architecture tuning, and evaluation.  
The conformity of the dataset partitioning to the IID assumption was evaluated by comparing the CDFs of the subsets using the KS statistical test. The KS test statistics were 1.8 \(\times\) 10\({}^{-6}\) for the training vs. validation datasets, 4.6 \(\times\) 10\({}^{-6}\) for the training vs. test datasets, and 5.72 \(\times\) 10\({}^{-6}\) for the validation vs. test datasets, all resulting in a \(p\)-value of 1.0. These low KS statistics indicate minimal differences in the distributions, and the \(p\)-value of 1.0 suggests no statistically significant differences among the dataset distributions. Therefore, the null hypothesis cannot be rejected, confirming that the training, validation, and test datasets are likely from the same distribution and adhere to the IID assumption.
# Revisiting the Detection of Lateral Movement through Sysmon
## 3 Preliminaries on Lateral Movement
The boundary between the internal network's perimeter and the outside world could be depicted as a horizontal line, in which the upper half comprises the outside world, whereas the environment under the line represents the closed, say, corporate network. For a malevolent intruder to penetrate a network, the movement should be executed vertically and towards the aforesaid horizontal boundary line. Mostly, this kind of movement is called \"North to South\" [18], and from a defender's viewpoint, it usually appears as an abnormality in the network's activity. When credentials are exposed, an anchor point is created, and the intruder can maneuver with privileged access horizontally and around the network components, gradually reaching and compromising the most valuable targets (\"East to West\" movement). Naturally, while it seems normal for a network component to communicate with certain terminals inside the same network domain, continuous connection attempts to open ports or manipulation of credential acquisition services via unregistered username or passwords are considered suspicious at least. Overall, there are two fundamental methodologies for moving laterally within a targeted network:  
* The first method refers to network mapping and identification of the special characteristics of each distinct component along with potential vulnerabilities. During the reconnaissance phase, many OS's built-in tools can be used. For instance, the ipconfig and ifconfig commands reveal IP configurations and localization information, while Netstat summarizes the most current network connections along with their users. Additionally, the local routing table tracks all the interconnected network paths, while the Address Resolution Protocol (ARP) cache reveals the matching of network IP addresses to their equivalent link layer (MAC) addresses. Vulnerability assessment and mapping tools are also in the attackers' quiver, namely Nmap or its commercial equivalent Nessus [19]. Once the relevant information is gathered, the adversary is able to intrude and move laterally. * The second customary method refers to the use of phishing techniques for the acquisition of account credentials by any means. To this end, the opponent can take advantage of tools, say, Metasploit Pro, to craft numerous phishing emails that have been perfectly cloned to appear as originating from a legitimate source. Additionally, keylogger tools, including Revealer Keylogger Free, KidLogger, and BlackBox Express, provide a powerful equivalent solution for activity monitoring and screen recording at the victim's side. Mimikatz, presented in Section 6.2, is another powerful credential exploitation and hash dumping penetration testing tool.
## 4 Results and Discussion
### Discussion  
In arch dam construction, thermal stress due to improper temperature control is a significant cause of concrete cracking. Although recent advancements in dam temperature control technology based on real-time monitoring have effectively mitigated harmful thermal stress, such techniques lack predictive capability. This deficiency limits the accurate provision of control parameters, thereby increasing the potential risk of cracking. To address this issue, this study developed a rapid and precise online temperature prediction model (SSA-ISVR). The model was validated through practical engineering applications.  
Sections 4.2.1 and 4.2.2 present detailed comparisons between offline and online models under various operating conditions. The results indicate that the proposed SSA-ISVR online model achieves superior prediction accuracy and generalization capability. It successfully captures time-varying system characteristics and employs sample selection mechanisms (Section 2.3) to regulate sample capacity during online learning. Consequently, the model maintains strong generalization and reduces redundant features or efficiency loss. Through data-driven methods, the model provides precise predictions of future system states, thereby optimizing decision-making, enhancing efficiency, and reducing risks. Thus, it shows broad potential applications in engineering control, transportation scheduling, and other dynamic system identification fields.  
This study has several limitations that warrant discussion:  
1. The SSA optimization method was exclusively applied to the ISVR model. Although the ISVR model without SSA optimization demonstrated satisfactory performance in experiments, the efficacy of SSA optimization for other models remains to be validated.
2. While SSA parameter settings are relatively straightforward and can be informed by prior research, the methodology for selecting optimal parameters has not been thoroughly explored.
3. The impact of sample size on model performance requires additional examination. Substantial sample sizes may introduce redundancy and reduce modeling efficiency, whereas insufficient sample sizes could affect the model's generalization capability.  
Future research should focus on extending the SSA optimization framework to multiple machine learning models and verifying its generality through quantitative comparisons. It should also focus on adaptive optimization of optimizer parameters and sample sizes to reduce dependency. Adaptive control strategies and reinforcement learning mechanisms can be introduced to reduce the dependence on SSA initial parameter settings and SSA sample selection.
# Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR
## 4 The Principle of Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR  
From the analysis of previous section, the ArcSAR imaging result on the reference plane is affected by the terrain fluctuation. For acquiring high precision ArcSAR image, an interferometric DEM-assisted high precision imaging method for ArcSAR is proposed in this paper. The interferometric ArcSAR is  
Figure 4: The \(h\)-\(\Delta R_{\max}\) relationship curve.  
Figure 5: Imaging simulation results of target P on the reference plane.  
utilized to acquire the scenes DEM on the slant range. Since DEM-assisted ArcSAR imaging requires the DEM image on the ground range, we propose a polar coordinate transformation method for transforming DEM image from slant range to ground range. With the assist of DEM image on the ground range, the target in the scenes can be imaged on its actual height.  
The steps of the above method are as follows: (1) The ArcSAR system is utilized to scan the same scenes at two different heights for getting ArcSAR Image 1 and ArcSAR Image 2. It should be noted that the acquired two ArcSAR images are the imaging results on the reference plane. Although the imaging accuracy of them is affected by the terrain fluctuation, their phase information can be used to obtain the interferometric phase. (2) ArcSAR Image 1 and ArcSAR Image 2 are used for interference to obtain interferometric phase. (3) We perform the operations of flat phase removing and phase unwrapping for the interferometric phase. (4) The unwrapped interferometric phase is used to inverse the DEM of the scenes. (5) The DEM image in slant range is transformed to the ground range by proposed polar coordinate transformation method. (6) The DEM of the scenes is used to assist ArcSAR imaging. A flow chart of the above method is shown in Figure 6.
# Explainable AI for DeepFake Detection
###### Abstract  
The surge in technological advancements has resulted in concerns over its misuse in politics and entertainment, making reliable detection methods essential. This study introduces a deepfake detection technique that enhances interpretability using the network dissection algorithm. This research consists of two stages: (1) detection of forged images using advanced convolutional neural networks such as ResNet-50, Inception V3, and VGG-16, and (2) applying the network dissection algorithm to understand the models' internal decision-making processes. The CNNs' performance is evaluated through F1-scores ranging from 0.8 to 0.9, demonstrating their effectiveness. By analyzing the facial features learned by the models, this study provides explainable results for classifying images as real or fake. This interpretability is crucial in understanding how deepfake detection models operate. Although numerous detection models exist, they often lack transparency in their decision-making processes. This research fills that gap by offering insights into how these models distinguish real from manipulated images. The findings highlight the importance of interpretability in deep neural networks, providing a better understanding of their hierarchical structures and decision processes.  
Keywords:explainable artificial intelligence; deep learning; deepfake detection; explainability; convolutional neural network; VGG-16; ResNet-50; inception V3; network dissection; face dictionary; model interpretability +
Footnote †: journal: Applied Sciences
# Surface Defect Detection of Rolled Steel Based on Lightweight Model
## 1 Introduction
Attention mechanisms such as SENet [18], ECANet [19], and CBAM [20] assist in improving the feature expression of small target detection networks, i.e., focusing on essential features and suppressing unnecessary features, which can effectively improve image classification and target detection performance. This research will employ the method of deep learning to detect surface defects on rolled steel. Based on YOLOv5s, an enhanced YOLOv5s algorithm is presented to improve the accuracy of rolled steel flaw detection and actualize the network's lightweight to satisfy industrial production needs and real-time detection requirements.