esa-sceva/satcom-chunk-collection huggingface dataset:
# Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges
## 8 Identifying Obfuscation Techniques: The Starting Point  
The journey of deobfuscation begins with identifying the specific techniques employed in the code. Obfuscation can take many forms, from string encryption, where readable text is scrambled, to code packing, which compresses the original code and wraps it in a decryption routine executed at runtime. Techniques like control flow obfuscation, which alters the logical flow to make the execution path difficult to trace, and dead code insertion, where unnecessary code is added, are also common. Recognizing these techniques is the first crucial step, as it guides the selection of tools and methods for the subsequent stages of deobfuscation. One way to remove some obfuscation artifacts in compile languages is to reverse engineer the compiled code. This, for example, will remove deliberately confusing variable names like \"10\", \"il0\", or \"11O\" by replacing them with var_12, var_13, or var_14.
## 2 Proposed Method
### Feature Fusion Module  
Convolutional neural networks aim to extract different features of the input image, an approach that gives a different response at each layer of the feature extractor. The input image contains a richer level of detail in the shallow features while the focus in the deep features is on the semantic information of the image. The semantic information of pavement cracks is relatively straightforward, while the texture and edge information are complex and diverse. The construction of the network is crucial to retain the details of the crack information.  
In this paper, we refer to the architecture of the U-Net [22] neural network and use a long connection structure to fuse the deep semantic information with the shallow edge information and to build the feature fusion module, whose structure is shown in Figure 6.  
The deeper the network, the larger the field of view of the feature map and the better the grasp of the low-frequency information of the cracks. In contrast, a shallow network  
Figure 5: Structure of the ACE module.  
Figure 6: Structure of the feature fusion module.  
has better control of the local information and focuses on the extraction of the crack details. However, the level of detail is bound to be lost in the process of downsampling and feature extraction. The long connection structure is used to stitch the feature maps obtained from the encode part, which introduces high-frequency information with high resolution and also provides the spatial information required at the upsampling stage, making the segmentation mask more accurate.
## 2 Related Work  
### Multiple Object Tracking  
Although a great deal of work has been done on multiple object tracking (MOT), it still remains a challenging task, especially in complex environments where occlusions and imperfect detections are common. Recent works on MOT primarily focus on the tracking-by-detection paradigm. Most of them can be roughly categorized into offline tracking and online tracking.  
In offline tracking, MOT is formed as an offline global optimization problem which uses frame observation through previous and future states to estimate the current status of objects [11; 12; 13; 14; 15]. A data association method is widely used, such as the Hungarian algorithm [16; 17], network flow [18; 19], or multiple hypotheses tracking [20]. However, offline methods are not suitable for causal applications like autonomous driving, since future information is necessary.  
Things are quite different in online tracking, where observations from before the current frame are provided to the online estimation of object states. Trajectories are generated based on information only up to the current frame, which adopt probabilistic inference [21] or deterministic optimization. However, online methods heavily rely on the performance of the underlying detector, since they are more sensitive to noisy detections. This is a resource waste of the tracker itself, since the detector is unable to access the temporal information from the previous trajectories. Our work focuses on extending online single object tracking (SOT) methods to MOT scenarios. SOT helps to alleviate the limitations from imperfect detections, especially for missing detections. It is complementary to the data association methods, since the tracking results of single object trackers at the current frame can be considered as association candidates for the data association.  
Figure 2: An example of our method. At time \(t\), the estimated bounding boxes provided by every single object tracker are sent to the binary-channel verification model along with the bounding boxes from the external detector, where the determination of the output is based on the evaluation score of the single object tracker alone. Then, a delay processing model focusing on potential complex circumstance like \(t+1\) above is proposed to handle occlusion, deformation, etc. Single object tracking (SOT).
# Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis
## 3 Methodology
### Direction-Aware Detection Head
#### 3.3.3 Joint Optimization Strategy  
To effectively train our direction-aware detection head, we employ a joint optimization strategy that balances the contributions of classification, regression, and direction prediction:  
\[\mathcal{L}_{total}=\lambda_{cls}\mathcal{L}_{cls}+\lambda_{reg}\mathcal{L}_{ reg}+\lambda_{dir}\mathcal{L}_{dir} \tag{15}\]  
where  
* \(\mathcal{L}_{cls}\) is the binary cross-entropy loss for text/non-text classification;
* \(\mathcal{L}_{reg}\) is the smooth L1 loss for bounding box regression;
* \(\mathcal{L}_{dir}\) is the cross-entropy loss for direction classification;
* \(\lambda_{cls}\), \(\lambda_{reg}\), and \(\lambda_{dir}\) are weighting coefficients determined by our adaptive weighting mechanism (described in Section 3.4.4).  
This joint optimization approach ensures that the model simultaneously learns to classify layout elements, localize their boundaries, and predict their orientations. The direction prediction particularly benefits the detection of vertical text regions in traditional Mongolian documents, significantly improving the overall layout analysis performance.  
The information sharing between branches creates a synergistic effect, where direction awareness improves region localization, and region awareness enhances direction prediction. This integrated approach effectively addresses the unique challenges of traditional Mongolian document layout analysis.
## 4 Practical Experiments
### Shisper Glacier  
Shisper glacier, situated in the Karakoram range of northern Pakistan, is known for its dynamic and fast-moving behavior, characterized by rapid changes and periodic surges. A notable surge in 2018 led to the formation of a large ice-damped lake, posing a significant threat to the nearby Ali Abad village due to the risk of catastrophic flooding from a potential outburst. This event has drawn considerable scientific attention, with monitoring efforts combining satellite observations and on-ground inspections to provide early warnings to local communities. The glacier's activity exemplifies the dynamic and unpredictable nature of Karakoram glaciers, influenced by climate change and geological processes, making it a key focus in glaciological research (e.g., [36; 37; 38; 39]). Ref. [37] highlighted the use of multi-temporal DEMs, derived from PlanetScope data, to analyze the 2018 surge event, referencing the methodology established by [16]. In their work, Shisper glacier was mapped in 2017 and 2019 to validate the proposed method (see Section 2) and to quantify the surge event using DoD analysis. This makes Shisper glacier an ideal case study for comparing _Planet4 Stereo_ with an established commercial-software-based method as both utilize the same PlanetScope scenes and nearly identical regions for accuracy assessment. For details on the scene IDs and data acquisition parameters, refer to Tables A1 and A2 in [16] (open access). The study area and cloud-free PSS images used for 3D modeling in 2017 (16 scenes captured over 11 days) and 2019 (14 scenes captured over 12 days) are shown in Figure 3 and Figure 4, respectively.  
Figure 3: Shisper glacier study area with Ali Abad and Karimabad village. PSS images used for DEM calculation in 2017 are marked, as well as areas that are further analyzed in Section 4.1.2.  
It is important to note that the scenes selected for 2019 differ by two scenes compared to [16]. Specifically, we focused on the flight strips of satellites 1021 and 1006, including images captured at 05:22:32 and 05:24:23, respectively. These scenes provide better coverage of the ROI and the Ali Abad village, facilitating validation purposes. Conversely, we excluded the scenes taken at 05:22:29 and 05:24:20, used by the original authors, as they primarily cover the northern glacier area outside the ROI. In [16], a presumably stable area of approximately 25 km\({}^{2}\) around Ali Abad village (outlined in red in Figures 3 and 4) was used to assess the accuracy of the generated DEMs. This same area was considered in our subsequent accuracy analysis.
## 3 Simulation Results and Discussion
### Ring-Type Cutoff Probe (RCP)
The RCP essentially featured resonance peaks induced by SW resonance in the simulated frequency ranges (\(<\)10 GHz), resulting from the length of the detecting antenna. This fact ultimately brought some negative aspects to the RCP, since the resonance peaks made finding the cutoff peak difficult in an S\({}_{21}\) spectrum, as discussed below. Figure 4 shows the S\({}_{21}\) spectra of the RCP with various electron density values ranging from \(1\times 10^{9}\) to \(1\times 10^{11}\) cm\({}^{-3}\) with various \(\beta\). In Figure 4a, an increase in \(\beta\) produced a high frequency shift of the cutoff frequency, which was the extreme in the S\({}_{21}\) spectrum and marked by an arrow. Eventually, the cutoff frequency became equal to the plasma frequency (\(f_{\rm pe}\) in Figure 4). This might have resulted from a larger rate of reduction in the capacitive coupling in the sheath (Path 1 in Figure 1c) compared to that in the plasma (Path 2 in Figure 1c) with increasing antenna distance [25]. In other words, at a short antenna distance, capacitive coupling in the sheath was dominant, so the effective electron density became smaller than the input electron density due to the sheath, where the electron density was zero. This means that the cutoff frequency was lower than the plasma frequency. But at longer antenna distances, coupling in the plasma was dominant, and the effective electron density became the input electron density, and thereby, the cutoff frequency matched the plasma frequency [26]. Deeper analysis of the different coupling reduction trends between Path 1 and Path 2 in terms of antenna distance is beyond the scope of this paper, but will be discussed in detail in a later paper with rigorous theory. The trend of cutoff frequency saturating to the plasma frequency at large antenna distances was the same as for other high density cases, as shown in Figure 4b,c. If measurement accuracy is defined as the discrepancy between the cutoff frequency (\(f_{\rm c}\)) and the plasma frequency (\(f_{\rm pe}\)) as \(\frac{f_{\rm pe}-f_{\rm c}}{f_{\rm pe}}\), the measurement accuracy of the RCP, therefore, strongly depended on the antenna distance; it was recommended that the distance be as large as possible. However, since the overall level of an S\({}_{21}\) spectrum diminished as the antenna distance increased, there was a trade-off between signal-to-noise ratio and measurement accuracy. Additionally, the larger the antenna distance was, the more complex the spectrum shape became due to the SW resonance. Figure 3: Normalized \(E_{z}\)-field distribution (\(E_{z}\)/\(E_{z,\rm max}\)) on the detecting antenna of the RCP along normalized antenna length (\(L\)/\(L_{\rm max}\)), where the z-direction is parallel to the normal direction of the antenna surface. Figure 4: S\({}_{21}\) spectra of the RCP from various normalized antenna distances (\(\beta\)) with electron densities of (a) \(n_{\rm e}=1\times 10^{9}\) cm\({}^{-3}\), (b) \(5\times 10^{10}\) cm\({}^{-3}\), and (c) \(1\times 10^{11}\) cm\({}^{-3}\).
# A Pseudo-3D Model for Electromagnetic Acoustic Transducers (EMATs)
## 2 Vertical Plane Modelling
### EMAT-US Simulation
#### 2.2.2 Combination of EMAT-EM and EMAT-US Models  
As described in Section 2.1.3, \(\mathbf{F}\) is obtained from the EMAT-EM calculation. In this section, \(\mathbf{F}\), which is used as the force source, is imported to the EMAT-US model to produce ultrasound (Figure 6). Since \(\mathbf{F}\) is calculated in the frequency domain and FDTD is a time-domain solver, the excitation signal for the EMAT-US model is a time sequence signal with the peak equalling the peak values of \(\mathbf{F}\). The excitation signal used is a Gaussian-modulated sinusoidal with a fractional width of 0.18. A crack and a receiver R are located within the sample, as shown in Figure 6. Regarding the FDTD setup in the ultrasonic model, there are two main parameters: the spatial step, and the time step. The spatial step used is 0.2 mm, which approximately equals to 1/30 th of the wavelength. The time step is 0.0222 \(\upmu\)s, which is calculated based on the Courant-Friedrichs-Lewy (CFL) condition. Free surface conditions are utilised on the surface of the sample. Perfectly-matched layers (PML) with a thickness of 16 mm are utilised to absorb ultrasound.
# Influence of TTS Systems Performance on Reaction Times in People with Aphasia
## 2 Materials and Methods
### Synthetic Speech Assessment
#### 2.3.1 Intelligibility  
In the current work, we assessed the intelligibility of a synthetic utterance by means of an automatic speech recognition system (ASR). As shown by the authors in [13], there is a correlation between the accuracy of an ASR system and the intelligibility of a speech utterance. All elements that may cause speech to be difficult to understand directly increase the error rate in an ASR system, such as mispronunciations, audio distortions, or inconsistent variations of speech loudness and rate. Therefore, we transcribed all of the synthetic audio samples with an ASR system, consequently measuring the word error rate (WER) plus the token error rate (TER) per sample. Note that since our ASR system outputs graphemes as tokens, our TER score is just the grapheme error rate. Lower error rates denote higher speech intelligibility, whereas higher error rates may reflect less intelligible speech.  
WER and TER measurements are computed by comparing the transcription from the ASR system with the ground truth human transcription. Both scores are based on the Levenshtein distance [14], which is the minimum number of edits that need to be done in a text sequence to match another. For example, the WER score is computed by counting the number of word substitutions (S), deletions (D), and insertions (I) needed to convert the ASR transcription into the ground truth one. Then, these counts are summed up, dividing the result by the number of words in the ground truth transcription.  
The computation of TER is done the same way, but by counting token edits instead of whole word edits. Regarding the ASR system specifics, we used a pre-trained transformer-based model from wav2 letter [15]. The model was trained with the Spanish Multilingual LibriSpeech dataset [16], and its recipe can be found in wav2 letter's repository. To obtain the transcriptions, we decoded the acoustic model outputs with the Viterbi algorithm, without any additional language model (LM). Given that we wanted to assess pure acoustic intelligibility, we avoided using an LM since it might correct some pronunciation mistakes that we want to detect.  
The WER and TER analyses were automatically computed. An input file with the.wav files--audio sentences--and the ground truth transcription was run through the handler programme, an executive file, and the ASR model (a previously trained deep learning model). The output file of this analysis reports (i) the hypothesis prediction text, namely, the ASR output for each audio sentence, (ii) the reference text (the ground truth), and (iii) the WER and TER for each sentence.
## 2 Simulation Setup and Estimation  
For this paper, we evaluate the impact of different high-precision observation techniques used by a future GNSS satellite constellation for the estimation of satellite clocks. The whole simulation and estimation procedure is equal to [17]. For the simulations and analysis, we used a modified version of Bernese GNSS Software 5.2 [25]. Next, we describe the satellite constellations used (Section 2.1), the simulated observation techniques (Section 2.2) and the measurement errors (Section 2.3) as well as the parameters estimated in the least-squares adjustment (Sections 2.4 and 2.5).
# Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning
## 2 Problem Formulation
 \tag{1}\]  
where \(r^{a}_{ss^{\prime}}\) is the immediate reward generated when state changes from \(s\) to \(s^{\prime}\) by taking action \(a\); \(C_{ICE}\) is the instantaneous fuel consumption value of the ICE; \(C\) is the numerical penalty, as well as the maximum instantaneous power supply from the ICE; \(Min_{C_{ICE}}\) is the minimum nonzero value of the ICE instantaneous fuel consumption value. The SOC variation range is from 40% to 85% in this study. This definition can guarantee the lower ICE fuel consumption while satisfying the SOC constrains. Formally, the goal of the EMS of the HEV is to find the optimal control strategy, \(\pi^{*}\), that maps the observed states \(s_{t}\) to the control action \(a_{t}\). Mathematically, the control strategy of the HEV can be formulated as an infinite horizon dynamic optimization problem as follows:  
\[R=\sum_{t=0}^{\infty}\gamma^{t}r(t) \tag{2}\]  
where \(r(t)\) is the immediate reward incurred by \(a_{t}\) at time \(t\); and \(\gamma\in(0,1)\) is a discount factor that assures the infinite sum of cost function convergence. We use \(Q^{*}(s_{t},a_{t})\), i.e., the optimal  
network are the system states, which are defined in Section 2. The rectified linear unit (ReLU) is used as the activation function for hidden layers, and the linear layer is used for obtaining the action value at the output layer. In order to balance the exploration and exploitation, the \(\varepsilon-greedy\) policy is used for action selection, i.e., the policy chooses the maximum Q-value action with probability \(1-\varepsilon\) and selects a random action with probability \(\varepsilon\). The Q-value estimation for all control actions can be calculated by performing a forward calculation in the neural network. The mean squared error between the target Q-value and the inferred output of neural network is defined as loss function in Equation (5):  
\[L(\theta)=E[(r+\gamma\underset{a_{t+1}}{\max}Q(s_{t+1},a_{t+1},\theta^{-})-Q(s _{t},a_{t},\theta))^{2}] \tag{5}\]  
where \(Q(s_{t}a_{t},\theta)\) is the output of the neural network with the parameters \(\theta\). \(r+\gamma\underset{a_{t+1}}{\max}Q(s_{t+1},a_{t+1},\theta^{-})\) is the target Q-value, using parameters \(\theta^{-}\) from some previous iteration. This fixed target Q network makes the algorithm more stable. Parameters in the neural network are updated by the gradient descent method. The inputs of DQN are total required torque \(T_{dem}\) and battery SOC. The variation range of SOC is from 0 to 1 and does not need preprocessing. However, the total required torque \(T_{dem}\) can vary significantly. In order to facilitate the learning process, we scale the total required torque \(T_{dem}\) to the range [\(-\)1, 1] before feeding to the neural network as shown in Equation (6). The minimum and maximum values for \(T_{dem}\) can be obtained from historical observation:  
\[T^{\prime}_{dem}=\frac{T_{dem}-\min(T_{dem})}{\max(T_{dem})-\min(T_{dem})} \tag{6}\]
# Numerical Evaluation of Structural Safety for Aged Onshore Wind Foundation to Extend Service Life
## 2 Service Life Extension of Onshore Wind Foundation
### Targeted Onshore Wind Foundation  
The spread footing is a common foundation type of the onshore wind turbine. This foundation type is subdivided according to how the tower is connected to the foundation [34]. There is an \"anchor ring-type\" in which the tower is embedded into the foundation, and an \"anchor rod-type\" in which the tower and the foundation are connected by anchor bolts. In this paper, structural safety evaluation was performed for the anchor ring type, as presented in Figure 4. This is because this type was applied to many sites during the introduction of onshore wind farms in South Korea, and the need to extend service life or re-use it has been steadily raised. The specification of the targeted wind turbine of this study was presented in Table 1, and the integrated turbine load was indicated in Figure 4a, where integrated turbine load means the ultimate (extreme) turbine load, including the safety margin of 1.3 as the load factor.  
Figure 3: The decision-making process for structural safety evaluation and re-use.  
The anchor ring type has the advantage of improving the overturning resistance of the tower because the bottom end of the tower is embedded in the foundation. However, it is known that the tower vibration caused by the turbine load directly acts on a connection part of the foundation, and this type of connection part is vulnerable to damage. In the past, the turbine capacity and the tower size were not large, so the turbine load acting on the connection part of this foundation was not large enough to damage the foundation. However, it has been known that in recent years, due to the enlargement of the wind turbine capacity, the turbine load acting on the connection part of the foundation increases, causing direct damage to the foundation of this type.  
\begin{table}
\begin{tabular}{c c} \hline
**Items** & **Specification** \\ \hline Turbine capacity & 660 kW \\ \hline Hub height & 45 m \\ \hline Bottom tower diameter & 3.0 m \\ \hline Operation year & 2003 \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of the wind turbine.  
Figure 4: Targeted onshore wind foundation model: (**a**) layout; (**b**) plan; (**c**) reinforcement; (**d**) anchor ring connection.