esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Traditionally, departments of transportation (DOTs) have dispatched probe vehicles with dedicated vehicles and drivers for monitoring traffic conditions. Emerging assisted GPS (AGPS) and accelerometer-equipped smartphones offer new sources of raw data that arise from voluntarily-traveling smartphone users provided that their modes of transportation can correctly be identified. By introducing additional raster map layers that indicate the availability of each mode, it is possible to enhance the accuracy of mode detection results. Even in its simplest form, an artificial neural network (ANN) excels at pattern recognition with a relatively short processing timeframe once it is properly trained, which is suitable for real-time mode identification purposes. Dubai is one of the major cities in the Middle East and offers unique environments, such as a high density of extremely high-rise buildings that may introduce multi-path errors with GPS signals. This paper develops real-time mode identification ANNs enhanced with proposed mode availability geographic information system (GIS) layers, firstly for a universal mode detection and, secondly for an auto mode detection for the particular intelligent transportation system (ITS) application of traffic monitoring, and compares the results with existing approaches. It is found that ANN-based real-time mode identification, enhanced by mode availability GIS layers, significantly outperforms the existing methods.  
artificial neural network; traffic monitoring; GPS; GIS; mode detection +
Footnote †: journal: Applied Sciences
## 3 System Model
### Feature Extraction and Distillation
#### 3.1.2 Channel Weighting Distillation  
In imagery, different features have varying levels of discriminative importance. Ubiquitous elements, such as textureless walls, ground, and sky, are less distinguishable as they appear repeatedly in different scenes, making it difficult to complete LCD tasks based on them. Conversely, objects that are infrequent in the environment, such as traffic signs and landmarks, exhibit higher distinctiveness. Treating features extracted from different distinguishable parts equally can result in more false positives in loop closure detection. Therefore, more attention should be given to the parts with higher distinguishability among the features extracted from the image, while the importance of parts with lower distinguishability should be reduced.  
Accordingly, to emphasize the imperceptible features of highly distinctive objects, we introduce a channel weighting mechanism in our FEN. Drawing inspiration from the approach in [44], we adopt a self-adaptive Channel Weighting Module (CWM), which is analogous to the Inverse Document Frequency (IDF) used in Bag-of-Words (BoW).In this context, a feature's discriminability is inversely correlated with its frequency in the dataset. The less frequently a feature appears, the more important it becomes for classifying images. By calculating adaptive channel weights, we focus on low-frequency features that are more discriminative. The formulation of the CWM is defined as follows:  
\[T_{c}=\frac{\sum_{h,w}^{F}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!  
After counting, \(T_{c}\) represents the mean positive response of the \(c\)-th channel feature. To accentuate less conspicuous features, the self-adaptive attention mask \(W_{c}^{m}\in R\) of the \(c\)-th channel is constructed using a \(log\)-based inverse function. This approach preferentially amplifies feature maps exhibiting lower responsiveness in the channel dimension. Based on that, our methodology facilitates selective feature enhancement.  
Then we can derive the weighted feature map \(F_{weight}\in R^{C\times H\times W}\) utilizing a self-attention mechanism across the channel dimension. The calculation process of \(F_{weight}\) on the \(c\)-th channel can be described as  
\[F_{weight,\ c}=W_{c}^{m}F^{c} \tag{3}\]  
Since the calculation of channel-wise attention masks completely depends on the model's response to visual features, there are no static trainable parameters in the self-attention mechanism. Therefore, CWM can dynamically obtain the channel-wise attention mask in a data-driven manner.
# APIASO: A Novel API Call Obfuscation Technique Based on Address Space Obscurity
## 5 Experimental Evaluation
### Model Protection Effect Evaluation  
The API address space is obscured using API code extracted from the system DLL, while the accuracy of this process still needs to be verified. The Windows API includes thousands of functions that can be called, and Microsoft officially classifies these functions into the following broad categories: basic services, component services, user interface services, graphics multimedia services, messaging and collaboration, networking, and web services. To cover the above types of services, the following 12 types of programs are implemented by using over 1000 Windows APIs: file handlers, network programs, message handlers, printers, text and font functions, menu handlers, bitmap, and raster arithmetic programs, drawing programs, device scenario programs, hardware and system programs, process and thread programs, and control and messaging programs.  
Figure 11 describes this verification process. The test process is based on LLVM and is divided into the following stages: (I) converting the source program containing the API calls into the corresponding intermediate language file Src.bc; (II) extracting the API code from the system DLL file using a decompiling tool; (III) completing the API replacement at the intermediate language level with LLVM, link the intermediate file; (IV) outputting the unobfuscated binary; (V) running the binary before and after the obfuscation to compare the two functions and verify the correctness of the API execution. In the end, the experimental results confirm the availability of APIASO.  
The APIASO is compared with the above three API call obfuscation methods to evaluate the program execution efficiency, and the obfuscated program is selected for six API magnitudes.  
For each test program, to accurately measure the running time of the program, each program was looped 100 times, and the average running time before and after the obfuscation was calculated.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**TypeProgram** & \multicolumn{6}{c}{**Sandboxie**} \\ \cline{2-7}  & **I** & **II** & **III** & **IV** & **V** & **VI** \\ \hline IAT redirection & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Position obfuscation & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ Call site tampering & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ APIASO & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sandboxie behavior monitoring results.  
Figure 11: APIASO availability testing.  
Figure 12 shows the time overhead before and after obfuscation for APIIASO versus the other three API call obfuscation methods. The \(x\)-axis represents each test program, and the \(y\)-axis represents the ratio of post-obfuscation to pre-obfuscation time overhead. The results show that APIIASO is close to the other obfuscation methods in terms of time overhead, and the overall obfuscation time overhead is no more than 20%.
## 5 Experiment Simulation
### Performance on the Tree Topology
#### 5.2.3 Comparison in Zipf Distribution Workload  
We further compare the proposed caching strategy with the state of the art in Zipf distribution workload. Figure 7 shows the comparison when \(\alpha\) changes, while Figure 8 shows the comparison when the cache size ratio changes. PB-NCC gets the highest cache hit ratio, the lowest server load, and most of the time the lowest latency. By local popularity estimation, PB-NCC keeps popularity contents in the cache. PROB shows the advantage of low latency when the cache size ratio is small, for the reason that PROB makes caching decisions based on the cache space available downstream. When cache space is small, PROB makes better use of limited cache space. As the cache size ratio increases, this advantage wears off. In tree topologies, both LCD and CL4M cache contents in the next hop of the hit node, so they share the same simulation results.  
Figure 5: Performance of different cache strategies in different node depths.  
Figure 6: Performance of different cache strategies in different node depths.
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
## 1 Introduction
The traditional velocity analysis in tunnels and travel-time tomography methods can only obtain the macroscopic velocity distribution and it is difficult to obtain high-resolution velocity models. FWI method is introduced into the tunnel seismic ahead prospecting method in this study. The frequency domain FWI method was used to study the wave velocity acquisition method of the rock mass in front of the tunnel face. The difference scheme is optimized to make it suitable for the complex environment of the tunnel. The forward data is used to verify the velocity results obtained by the FWI, and a more reliable wave velocity distribution is obtained.
# Evaluating the Effectiveness of Designs for Low-Cost Digital Manufacturing Systems
## 4 Development of the Evaluation Testbed
### Verification and Validation of Designs  
Before the candidates can be evaluated, it is necessary to assess if the reference architectures have been adopted accurately and if the resulting digital systems work as expected, which is commonly known as _design verification and validation_. Each design is _verified_ by assessing to which degree the designed system complies with the implementation guidelines and descriptions imposed by the reference architecture [53]. Architectural compliance is achieved when all features that have been designed based on a reference architecture are covered by and are in accordance with its specification. However, not all features need to be used to reach architectural compliance. This study assesses the architectural compliance (i.e., verifies the reference architecture designs) via a checklist of characteristics and implementation guidelines for each reference architecture.  
Additionally, each design is _validated_ by testing the functionality, interoperability and performance of the digital system as a result of adopting a particular design approach: the functionality is tested by checking the specifications of a digital system against its implemented features and functions. The interoperability is assessed by performing a series of tests to gauge certain interoperability aspects, such as the ease of information exchange between architectural elements and the consistency in terms of common programming styles and variable naming conventions. The performance of a digital system is tested by selecting certain parameters based on its specifications, such as the accuracy or range of a sensor, and testing the system against these parameters.
# Autonomous Obstacle Avoidance with Improved Deep Reinforcement Learning Based on Dynamic Huber Loss
## 3 A DRL Framework Based on Dynamic Huber Loss and Soft Updates
### Soft Update  
In the DRL field, the objective network's role is to provide relatively stable objective values to assist in the computation of the loss function, which helps the online network to learn better and optimize the strategy. Hard updating is to directly replace the target network parameters after a specific training step or cycle, which can cause large jumps in the target value during the model decision-making process, interfering with the normal learning of the online network, and making the learning process unstable or even unable to converge; it also suffers from the problem that the online network is easily over-adjusted or incorrectly adjusted due to the sudden changes in the target value [23]. Instead of directly replacing its parameters, Soft updating is performed according to the following equation:  
\[\theta^{-}=\tau\theta+(1-\tau)\theta^{-} \tag{18}\]  
where \(\tau\) is a Soft update factor taking a value between 0 and 1. The target network parameters are weighted combinations of the two, allowing the target network to be updated gently toward the online network parameters. The Soft update helps keep the target network parameters relatively stable, making the learning process smoother, improving training stability, reducing oscillations, and promoting model convergence [24]. Additionally, they ensure that the updates of the target and online networks work closely together, with the target network smoothly guiding the learning of the online network. This reduces ineffective exploration due to sudden changes in the target value and improves overall learning efficiency [25; 26].
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## 2 Effective Filtering Approach (EFA)
### Filtering Unicast Traffic in DANH Rings  
EFA uses PL for filtering unicast frames for nondestination DANH rings. Nondestination DANH rings are those DANH rings that do not contain the destination DANH. In the sample network  
Figure 1: Sample HSR network. HSR: high-availability seamless redundancy; DANH: doubly-attached nodes with HSR protocol.  
shown in Figure 1, DANH ring 3 is the destination DANH ring, whereas the other DANH rings are nondestination DANH rings.  
PL uses the first frame sent by the source DANH to detect nondestination DANH rings. When the source DANH sends the first frame to the destination DANH in an HSR network, the frame is forwarded to all DANH rings of the network. The frame is then circulated in all DANH rings except the destination DANH ring, as shown in Figure 2. By checking the circulation of the first frame, PL discovers nondestination DANH rings. PL then locks nondestination DANH rings to prevent the next frames from being forwarded to the DANH rings. From the second frame onward, EFA does not forward the unicast frame to nondestination DANH rings [13].
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 2 Related Work
The results of the experiments show the outperformance of the method in contrast to the existing algorithms. The research study [45] proposed new development of SMOTE by merging it with the Kalman filter. After applying SMOTE to the given dataset, the implemented algorithm, called Kalman-SMOTE (KSMOTE), excludes the noisy samples in the resultant dataset that simultaneously contains the initial data and the synthetically added samples. The method was examined on a broad range of datasets, and the results show that the implemented algorithm outperforms the existing methods. Since oversampling techniques cannot usually achieve high performance in the presence of noise, the study [46] implemented an innovative oversampling algorithm called IR-SMOTE that handles this issue. By sorting the majority class samples and the k-means clustering algorithm, the noise in minority class clusters is eliminated. After that, using the kernel density estimation method, the amount of synthetic samples is compatibly designated to each cluster. Finally, regarding random-SMOTE, the desired algorithm was improved to add new samples with ensured diversity. The literature [30] studied the performance of convolutional neural networks (CNNs) in the presence of imbalanced data for classification problems. In order to explore this probable impact, the research used MNIST, CIFAR-10, and ImageNet as benchmarks, alongside undersampling, oversampling, two-phase training, and thresholding. The results show that imbalanced data have a detrimental effect on the performance of the proposedmethod. Furthermore, one should implement oversampling to the level that removes the imbalance, while the extent of the imbalance determines the ideal undersampling ratio. In addition, oversampling does not lead to the overfitting of CNNs. Fault diagnosis of complex equipment, which plays an important role in industries, is a crucial technology, and CNN is a general tool for this purpose. In this case, faults are not common, which leads to imbalanced data, and therefore, one cannot propose CNN methods directly. In order to address this problem, a hierarchical training CNN is implemented in [47]. At first, the method uses a number-resampling technique to balance data. Then, a magnet-loss pretraining algorithm is provided to handle the overlap between diverse faults. The proposed method was examined on the public dataset CWRU with an accuracy of 94.28%.
## 3 Results and Discussion
### Encapsulation Efficiency and In Vitro Release Study
A higher drug release from matrices synthesized in the absence of an acid catalyst may be attributed to the differing porosity of the structure obtained during the synthesis process. The absence of an acid catalyst during the sol-gel synthesis of SiO\({}_{2}\) leads to a slower and less controlled formation of the matrix structure, resulting in a greater variety of pore sizes and distribution. This increased porosity facilitates drug release as it provides more space for the diffusion of drug molecules through the matrix. Conversely, the use of HNO\({}_{3}\) during synthesis results in the formation of a weakly branched -O-Si-O-based structure, characterized by a more closed and compact microstructure that partially impedes drug release [62]. Neither of the two systems is capable of fully releasing the encapsulated drug, indicating that a portion of the molecule is stacked in an inner part of the matrix in the form of aggregates, as previously reported in one of our earlier works [25]. After 24 h of the release test, pH values of the medium were calculated, and the results are reported in Table 2. The findings indicate that no pH variation occurred during the release process, confirming that the differential drug release capacity in the medium depended on structural differences in the inorganic matrix and the molecule subjected to oxidative processes rather than pH variations.
## 2 Related Work  
As GPS data collection is more accurate and reliable than traditional self-reporting, the use of GPS is common in the field of transportation and tourism [8]. Various studies have analyzed tourist behavior using GPS trajectory data in the transportation and tourism fields [8; 9]. [PERSON] et al. investigated traveler behavior using GPS in case studies of national park visitors [10]. [PERSON] and [PERSON] examined car-based tourism in a national park in Scotland and mapped the itineraries of the tourists [11]. They indicated that itinerary mapping could help policy makers understand spatial patterns for tourism planning. [PERSON] et al. investigated the spatial-temporal patterns of vehicular stopping behavior along park roads, indicating that such information is valuable to park managers to better understand and manage visitor flow [12]. [PERSON] et al. highlighted the tendency of additional stopover behavior during different departure periods under various congestion conditions, stating the possibility of promoting additional stopovers by providing near-future traffic congestion information [7]. The findings of these studies can help policy makers or managers promote tourism. Nevertheless, these implications were discovered in observations without actual interventions; therefore, it is difficult to conclude that these interventions are effective. The present study tested the effect of interventions via a field experiment.  
Mobile applications are used for interventions in broad research areas such as promoting healthy [3; 4] and pro-environmental behaviors [13; 14] and traffic and tourism research [5; 6]. However, a mobile application for stopover promotion has not been designed thus far. [PERSON] et al. described the user-centered design of a mobile application to promote sustainable behaviors in urban mobility, thereby demonstrating that not all users are motivated by environmental concerns and that users desire concrete rewards that are closely related to the target behavior [5]. [PERSON] and [PERSON] listed several mobile applications that could contribute to resolving traffic problems [6]. Certain mobile applications provide real-time traffic information and can reduce traffic congestion. For example, Sigalert.com [15] provides real-time updates on traffic and road speeds for the U.S. 511 Georgia and Atlanta Traffic [16], which is the official traffic application of the Georgia Department of Transportation (DOT). Colorado Roads [17] provides real-time information related to highways in the U.S. state of Colorado such as speeds, travel times, road conditions, incidents, and road closures. However, although such applications are commercialized and found in application stores, none of them target the behavior change of promoting stopovers.
## 3 Methodology
### Tuning the Forecasting Methods
#### 3.5.1 Statistical Methods Parameters  
* ARIMA. To find the best set of parameters for the ARIMA model, we performed a grid-search by trying different combinations of values for its three parameters \(a\), \(d\) and \(q\). The grid-search algorithms finds the best set of parameters for ARIMA by using as an optimization criteria the Root Mean Square Error of the residuals of the fitting process. Only the set of parameters yielding the lowest Root Mean Square Error was picked for performing forecast.
* Exponential Smoothing. As with what we did with ARIMA, also for ETS we tuned the parameters of the model by performing a grid-search with as criteria of optimization the RMSE error. Also in this case, only the set of parameters that yields the lowest RSME is picked, and this was done for every time series of each cell in the grid.
* Autoregression. For this forecasting model the Statsmodels library feature allows automatically selecting an appropriate lag value in order to make predictions.
* Prophet. For Prophet, instead, we did not optimize any parameter, but simply relied on the capability of its implementation to adapt to data as described by its creators in [9].
## 3 Test Results
### Splitting Failure of the Tunnel-Surrounding Rock  
To accurately estimate the splitting failure condition of the tunnel-surrounding rock, the specimen wreckage and rock chips were collected (shown in Figure 6).  
Figure 6a shows the results of the cavern-free sample. The splitting failures on both sidewalls present the same distribution when the sample only contains a prefabricated tunnel, and there is no rock ejection or large secondary crack in the top vault. The maximum thickness of rock blocks is about 3 mm, and the corresponding length is about 5-15 mm; the relatively larger rock blocks distribute near the tunnel free face (rock blocks are placed independently on the left bottom). On the other hand, small-size rock debris occurs at the inside rock, and the corresponding length is about 1-3 mm (rock debris is placed independently on the right bottom).  
Some special results are obtained from the cavern-existing sample (Figure 6b). The left and right sidewalls show different failure patterns after the true triaxial test, and the secondary failure starts to locate at the tunnel vault. The left sidewall (no cavern) suffers relatively mild destruction, and many of the broken rock blocks are still attached to the sidewall; the corresponding length is less than 5 mm, and the thickness is less than 2 mm. Because the prefabricate cavern is located outside the right arch, rock blocks near the cavern present a relatively large volume; the average length of the rock blocks is bigger  
Figure 5: Selected frames of cracking damage in the cavern-free (left part of semifigures (**a–d**) and cavern-existing (right part of semifigures (**a1–d1**)) samples.  
than 5 mm, and the corresponding thickness is 2-4 mm. In addition, the most intensive failure distributes near the cavern because the three largest-sized fragments appear at the middle position of the right sidewall.