esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Pre-Processing  
First, the datasets are pre-processed to remove high-frequency noise. The signal noise comes from the sensor itself or from the movement of the body. To remove the noise signal, we use a first-order low-pass Butterworth filter [31; 32]. Despite the fact that the second-order Butterworth filter provides better results, the first-order low-pass filter is selected because it requires fewer computations and is efficient enough to remove fall-activity-related signals.  
Feature normalization is a data pre-processing method that is used to map two or more sets of feature values, with disparities in their respective ranges, to a common range. The variations in accelerometer and gyroscope units such as revolutions per second are unlikely to be equivalent. Furthermore, feature normalization enables our model to converge rapidly while ensuring that the contribution of the feature is equivalent [33; 34; 35]. Thus, it is necessary to normalize the sensor measurements that have been selected as inputs to our model. It appears that the way of normalizing the features plays an important role in the overall success of the performance of our algorithm. In our approach, the data points for each feature are normalized using the minimum and maximum of each feature. The feature-normalization pre-processing is performed on the 3D acceleration signals \(A(t)=[A_{x}(t),A_{y}(t),A_{z}(t)]\) and 3D gyroscope signals \(G(t)=[G_{x}(t),G_{y}(t),G_{z}(t)]\). The general formula is given as follows:  
\[\widehat{feat}=\frac{Feat-min(Feat)}{max(Feat)-min(Feat)} \tag{1}\]  
where Feat is the vector of features x, y, and z of the accelerometer and gyroscope, and \(\widehat{Feat}\) is the normalized feature vector calculated using the maximum and minimum values of the vector of features.
## 5 Real-World Application: Government Debt Analysis
### Implementation and Results
Complete coverage (100%) across all pairwise comparisons ensures comprehensive assessment of inter-country fiscal policy relationships, providing robust foundation for comparative economic analysis. The distance analysis reveals several important patterns:  
1. Most Similar Fiscal Trajectories: Switzerland and Norway exhibit the highest similarity (Regular: 0.5082, Modified: 0.3197), despite Norway's oil wealth, suggesting convergent fiscal management approaches among resource-rich and traditionally conservative economies. 2. Greatest Trajectory Divergence: The United Kingdom and Denmark demonstrate the most divergent patterns (Regular: 0.9365, Modified: 0.7778), reflecting fundamentally different approaches to fiscal policy and economic management over the observation period. 3. Moderate Convergence Clusters: Germany demonstrates relatively similar trajectories to France (0.5397 regular distance) and Denmark (0.5873 regular distance), suggesting some convergence in European fiscal approaches despite different starting positions. 4. Exceptional Trajectory Patterns: Japan's unique fiscal evolution results in high distances with most countries, particularly Norway (1.0164 modified distance), reflecting the singular nature of Japan's prolonged debt accumulation following its economic bubble collapse. The high aggregate distances (0.7186 regular, 0.5624 modified) confirm substantial heterogeneity in fiscal trajectory evolution, validating the discriminatory power of the Hamming diversification index in capturing meaningful differences in long-term fiscal policy patterns across developed economies.
## 5 Discussion
### The Output Selection of FEM for Lightweight Model Design  
In this study, MobileNetV3 was employed as a fixed feature extractor, responsible for extracting fundamental visual features from the input data without participating in the training process. This design ensures consistent feature extraction across all viewpoints, eliminating potential inconsistencies caused by parameter updates during training. Furthermore, the output size of the FEM can be flexibly adjusted among the following configurations: \(7\times 7\times 160,14\times 14\times 112,28\times 28\times 40\), and \(56\times 56\times 24\). The relationship between FEM size, total parameters, and trainable parameters is summarized in the Table 5.  
The output size of the FEM determines the dimensions input into the subsequent Sparse Structural Attention module, which significantly affects the total and trainable parameters of the model. In particular, the SSANet relies heavily on the spatial size \(N\times N\) of the input; the dimensions \(N_{q}\), \(N_{k}\), and \(N_{v}\) of the Query, Key, and value are all equal to \(N\times N\); and as the feature map size increases from \(7\times 7\) to \(56\times 56\), the quadratic growth in \(N^{2}\) leads to an exponential increase in parameters. At \(7\times 7\), \(N^{2}=49\) results in a lightweight model with only 6.1 M total parameters. But at \(56\times 56\), \(N^{2}=3136\) causes the total parameters to surge to 152.4 M  
To strike a balance between performance and complexity, we selected \(7\times 7\times 160\) as the FEM configuration. Our model's total parameter count is only 6.1 M, with 1.5 M trainable parameters, significantly reducing the computational and storage requirements.
# An Experimental Study of Glycerol Carbonate Synthesis over g-C\({}_{3}\)N\({}_{4}\) Catalysts
## 2 Experimental
### Procedure for Catalytic Test in the Glycerol Carbonylation Reaction  
A mixture of 25 g of glycerin and 50 mL of acetonitrile, used as a dehydrating agent to shift the unfavorable equilibrium towards the product side and improve the selectivity for glycerol carbonate [13; 45], was added in the reactor. The catalyst was added at room temperature and then the temperature gradually increased to the desired value while the glycerin-catalyst-acetonitrile mixture was mechanically stirred. Subsequently, carbon dioxide gas was introduced until the desired pressure was reached. The reaction was allowed to proceed for 4 h.  
After each experiment, the autoclave was cooled to ambient temperature, depressurized, and the liquid phase was isolated from the catalyst through centrifugation. Further, the catalyst was rinsed with an ethanol-deionized water mixture, then dried at 80 \({}^{\circ}\)C overnight. The liquid phase was analyzed by gas chromatography flame ionization detector (GC-FID, Varian CP-3800, Varian. Inc., Walnut Creek, CA, USA) equipped with a ZB-5 ms column (L = 30 m, D = 250 \(\upmu\)m, d = 0.25 \(\upmu\)m) and a carrier gas (N\({}_{2}\)) flow rate of 1 mL/min. To determine the concentrations of glycerol carbonate a calibration curve was constructed using acetonitrile as the solvent. The curve was generated over a concentration range of 1 to 90% by weight and is provided in one of our recently published papers [46].  
The yield was evaluated by the following equation:  
\[\%\text{Yield of GC}=\frac{\text{moles GC}}{\text{moles GLY introduced}}\]
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 6 Discussion of Simulation Results
### Impact of Traffic Load and Placement of Nodes  
For a fair comparison and a more profound understanding, it is essential to study both the impact of the traffic load and placement of nodes. The Section 6 graphs use placement 1 to refer to the scenario where the sink node is placed at the top of the grid, and placement 2 to refer to the scenario where the sink node is placed in the center of the grid.
# An Approach to Designing Critical Railway Voice Communication
## 3 Reusable Functionality Related to Security Services
### Policy-Based Railway Communications  
To ensure reliable, safe, secure, and deterministic services under all conditions, even in cases of malfunctions related to infrastructure or train delays and cancelations, railway operations must follow certain rules [24]. Among others, the operational rules include the provisioning of information about train movements (e.g., the active tracking of train movements and the reporting of the location coordination, capacity restrictions, etc.) and the traceability of information and decisions (e.g., traffic control, corridor coordination, movement priority between trains, traffic diversion, and others). The control of infrastructure facilities on the railway network follows the rules established for their use. There are also rules for maintaining network availability, e.g., rules governing the troubleshooting of trains and rules to restore the infrastructure in the case of a train derailment or breakdown or urgent trackside repairs. The management of downgraded and emergency situations is also subject to rules, which aim to restore normal operation as quickly as possible, as well as alert and continuously inform responders. An approach to designing operational rules for a railway interlocking system is proposed in [25]. A methodology for the verification of the interlocking system according to international signaling rules is presented in [26]. The authors of [27] present a method for the optimization of operational rules for heavy-haul trains.  
The use of FRMCS critical communication and performance applications is also based on rules. In the case of simultaneous connections that compete for user attention, an arbitration function is needed. Multiuser talker control functionality is required for multiparty voice communications to mitigate the risk of miscommunications, e.g., for emergency communications, shunting communications, banking communications, and communications between trackside workers. Functions for arbitration between competing communications and multiuser talker control are defined as a key component of critical railway applications [1].  
The functions for application authorization, communication authorization, arbitration, and multiuser talker control are based on predefined policies. The policies outline the operational criteria and are configured by the railway operator. In order to manage (create/update/retrieve/delete) the policy information, control access to it, and request a policy evaluation, a policy service is needed.  
The proposed policy API is described in the next section.
## 6 Simulation Results
### Delivery Rate  
The delivery rate is defined as the ratio of the number of successfully received search packets at the mobile actor to the total number of packets generated by the source. The velocity of the actor is fixed at 3.5 m/s. The analysis in Section 5.1 has stated that the trail connectivity has tight relation with the deliver rate, and the footprint update interval \(T_{u}\) needs to satisfy \(T_{u}\leq\sqrt{2}\cdot\frac{L}{v}\) to guarantee the connectivity of the trail. Therefore, we study the deliver rate with different footprint update intervals.  
As shown in Figure 6, the update interval \(T_{u}\) varies from 1 to 25 s. According to Theorem 1, we obtain the upper bound of \(T_{u}\) that ensures a connected trail in the given simulation setting is 8.08 s. The result shows that the delivery rate is guaranteed when \(T_{u}\leq 10\). The delivery rate starts to descend when \(T_{u}>10\) and drops dramatically after that. The simulation result proves the correctness of Theorem 1, and provides the guideline of setting footprint update intervals in the following simulations.
## 2 Materials and Methods
### Microbiological Analysis  
To isolate and enumerate the microorganisms listed below, ten grams were taken representative of each sample (SM and SR) and placed in sterile stomacher bag. Ninety milliliters of sterilized peptone water (PW; Oxoid, Madrid, Spain) were added to each bag (1:10, \(w/v\)), and then, the content was homogenized for three minutes at 230 rpm using a peristaltic homogenizer (BagMixer\({}^{\text{\textregistered}}\)400 P, Interscience, Saint Nom, France). Subsequently, ten-fold serial dilutions were prepared, and microorganisms were cultured as follows: (i) Total mesophilic aerobic bacterial counts (TAB 30 \({}^{\circ}\)C) were determined using plate count agar (PCA; Oxoid, Madrid, Spain) and incubated at 30 \({}^{\circ}\)C for 48-72 h [14]; (ii) \(\beta\)-glucuron-positive _Escherichia coli_ were isolated on Tyrptone Bile x-Glucuronide (TBX; CM0945, Oxoid, Basingstoke, Hampshire, UK) and incubated at 44 \({}^{\circ}\)C for 24-48 h [15]; (iii) _Pseudomonas_ spp. were cultured on CFC agar (Cetrimide-Fucidin-Cephalothin agar with a modified CFC selective supplement, SR0103E; Oxoid, Basingstoke, UK) and incubated aerobically at 25 \({}^{\circ}\)C for 48 h [16]; (iv) mesophilic lactic acid bacteria (LAB) were enumerated using De Man, Rogosa, and Sharpe agar (MRS, CM0361; Oxoid, Hampshire, UK) and incubated aerobically for 72 h at 30 \({}^{\circ}\)C [17]; (v) yeasts and molds were detected on Dicloran Rose-Bengal Chloramphenicol Agar (DRBC; Oxoid, Madrid, Spain) incubated at 25 \({}^{\circ}\)C for 120-168 h [18]. After incubation and counting, the data were expressed as logarithms of the number of colony-forming units (Log CFU g\({}^{-1}\)), and the means and standard deviation were calculated.  
Research of pathogenic bacteria was conducted to detect _Listeria monocytogenes_, _Salmonella_ spp., _Clostidium perfringens_, and _Bacillus cereus_. To detect _Listeria monocytogenes_, 25 g of salami samples (SM and SR) were homogenized in 225 mL (1:10, \(w/v\)) of half Fraser broth (HFB, CM1053, Oxoid), spread on ALOA petri dishes (Agar Listeria according to [PERSON] and [PERSON]), and then incubated at 37 \({}^{\circ}\)C for 24 h [19]. For _Salmonella_ spp., homogenization of 25 g of sample in 225 mL (1:10, \(w/v\)) of buffered peptone water (BPW; Oxoid, Madrid, Spain) was required, followed by transfer to Rappaport-Vassiliadis broth (RVS) and incubation at 41.5 \({}^{\circ}\)C for 24 h. Subsequently, the samples were plated on xylose lysine deoxycholate (XLD; Oxoid, Hampshire, UK) agar petri dishes and incubated at 37 \({}^{\circ}\)C for 24 h [20]. For _Clostridium perfringens_[21] and _Bacillus cereus_[22], 1 mL of the solution prepared with peptone water (PW; Oxoid, Madrid, Spain) was inoculated on petri dishes and then incubated anaerobically at 37 \({}^{\circ}\)C for 18-24 h for both bacteria.
# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 2 Related Works
### Dilated Convolution  
Dilated convolution, also referred to as atrous convolution [19], was introduced during the International Conference on Learning Representations (ICLR) in 2016 as a solution to the problem of feature information loss induced by pooling layers in image segmentation algorithms [20].  
Figure 1: Densely connected convolutional networks.  
In image segmentation tasks, it is noteworthy that the spatial scale of the input and output of the neural network remains consistent. Following the application of multiple pooling layers, the resolution of feature maps diminishes, necessitating upsampling to align with the spatial dimensions of the input image. This process results in a partial loss of feature information. In contrast to conventional convolution, dilated convolution enlarges the convolution kernel by incorporating gaps for convolution operations between the kernel. This expansion enhances the receptive field without necessitating an increase in the number of parameters.  
Figure 2 illustrates the schematic diagrams depicting traditional convolution [21] and dilated convolution. In conventional convolution operations, the connections are dense, with each element of the convolution kernel interacting with adjacent elements on the input feature map. Dilated convolution, in contrast, introduces \"gaps\" between the elements of the convolution kernel, enabling each pixel of the kernel to interact with spaced elements on the input feature map. This spaced sampling expands the \"receptive field\" of the convolutional kernel without augmenting the parameter count.
# An Efficient Steganographic Protocol for WebP Files
## 2 Materials and Methods
### Framework  
The framework of proposed methods consists of two complementary algorithms: embedding and extracting. The embedding algorithm takes the WebP carrier, secret message, and optional key as input. The message is hidden in the image, and the resulting file is transmitted to the receiver. Then, the container with hidden data (optionally with the key) is given to the extracting algorithm that retrieves the secret message out of it. The schema of the whole process is presented in Figure 2.  
As can be seen in the schema, the WebP carrier consists of a chunk containing metadata and other file-related information, and an image chunk containing compressed image data. Presented in this paper are format-based methods relying on the chunk part, whereas data-based techniques are focused on modifications of image data.
## 1 Introduction
It automates the quantitative analysis of spontaneous speech and language to assess a wide range of neuropsychological impairments including aphasia, dementia, schizophrenia, autism, and epilepsy [13]. However, most of the proposed software solutions are bound to one language, which hinders generalizability in multi-lingual communities [11; 14; 15]. Thus, there is a need for a generalizable automated software solution competent of making efficient and accurate diagnosis decisions. Hence, developing an automated solution that classifies temporoparietal aphasia using acoustic frequencies would be a promising approach towards the betterment of clinical diagnostics. In fact, the analysis of acoustic frequencies of speech has been considered as a promising technique to implement non-invasive and objective speech and language assessments [16]. In addition, ASR free feature analysis improves accuracy, while reducing language dependency bound with ASR techniques [16]. Nevertheless, the clinical application of automated aphasia assessment is minimal due to the lack of accessibility, appropriateness, availability, and monetary restrictions. Hence, this article demonstrates a hybrid software solution that incorporates acoustic frequencies of speech signals to determine and distinguish aphasia. In fact, the utilization of ASR free techniques augments the generalizability, since acoustic frequencies are not language dependent. The developed system consists of three diagnosis components, i.e., confrontation naming, single word repetition, and comprehension analysis. The hybrid system automates naming and repetition tasks. Meanwhile, quantitative measurements of the patients' comprehension levels were taken into account for the comprehension analysis. Introduced mathematical relationships among identified parameters derive a score for each assessment component. Accordingly, the proposed algorithm determines presence of aphasia or/and a differential diagnosis for each participant. Finally, a diagnosis report is generated for the reference of SLPs. As a pilot study, this work aims to distinguish aphasia occurrence with temporoparietal lesions, which are basically Anomic aphasia and Wernicke's aphasia. The conceptual breakthrough of the proposed work is in distinguishing two types of aphasia using acoustic frequencies of pathological speech production and comprehension analysis. To the best of our knowledge, previous works have not developed a language independent hybridized software solution to classify subtypes of aphasia using acoustic frequencies of speech signals.
# Effect of the Depth of Decarburized Layer in SKL15 Tension Clamp on Fatigue Strength
## 2 Investigation of Cause of Fatigue Crack  
### Result of Decarburized Layer Measurement  
To determine the decrease in the fatigue strength of SKL 15, the depth of the decarburized layer was assessed by measuring the hardness from the cross-section to the depth. The depth of the decarburized layer can be measured by measuring hardness and by evaluating the picture of the tissue of the fracture surface with a microscope. In general, for quenched and tempered steels, the depth of the decarburized layer is determined by measuring the Vickers hardness [4]. The results of measuring the Vickers hardness of fracture surfaces of eight cracked tension clamps and one uncracked tension clamp are shown in Figure 2. The hardness was measured at 30 \(\upmu\)m intervals from the surface, and the depth of the decarburized layer was considered to extend to the point at which the hardness was 90% of the difference between the surface and the base, metal as shown in Equation (1), according to [PERSON] [5].  
\[HV_{d}=0.9\times\ (HV_{B}-HV_{S})\ +\ HV_{S} \tag{1}\]  
where \(HV_{d}\) is the Vickers hardness at the end of the decarburized layer, \(HV_{B}\) is the Vickers hardness of the base metal, and \(HV_{S}\) is the Vickers hardness at the surface. Thus, the depth of the decarburized layer was determined at the distance from the surface that  
Figure 1: (**a**) Fractured tension clamp; (**b**) fatigue fracture surface.  
matched \(HV_{d}\). The depth of the decarburized layer was not constant and showed a significant deviation. The minimum and maximum depths of the cracked specimens were 0.17 and 0.36 mm, respectively. The decarburized layer depth of the uncracked specimen was 0.12 mm. For reference, the maximum decarburized layer depth suggested by the manufacturer is 0.2 mm. With the exception of one of the eight specimens, the depth of the decarburized layer of all specimens was thicker than the manufacturer's allowable depth. Because the fatigue failure occurred even in the tension clamp with the thinnest decarburized layer, it is necessary to confirm the validity of the manufacturer's allowable depth. Figure 3 shows pictures taken with a scanning electron microscope (SEM) of the decarburized layers of Specim #7 and the uncracked specimen. The white layer indicates the area that was completely decarburized. As is clearly shown in the figure, the depth of the white layer of Specim #7 was much deeper than that of the uncracked specimen.
## 3 Proposed Current Detection Schemes with Reduced Sensors
### Current Control without Sampling Demagnetization Current
#### 3.4.2 Voltage Pulse-Width Modulation Control (VPC)  
This scheme controls the current by regulating the average phase voltage through PWM. An average voltage can be generated by applying positive and negative dc-link voltages at different times of a PWM cycle. A positive average voltage can also be generated by applying positive dc-link voltage and zero voltage at different times, while a negative average voltage can also be generated by applying negative dc-link voltage and zero voltage at different times. The voltage generation with zero voltage is preferred due to fewer switching times and smaller current ripples. The phase current sampling can be performed at the overflow or underflow of the PWM counter, and the duration of each sampling is usually far shorter than the PWM cycle.  
In order to apply VPC without sampling demagnetization current, a PWM configuration scheme with a current sampling timing scheme based on the configuration is proposed and shown in Figure 10, where \(CR_{1}\) and \(CR_{2}\) are the comparison values for the drive signals \(DS_{1}\) and \(DS_{2}\), respectively; \(CTR\), \(M_{\text{CTR}}\), and \(T_{\text{P}}\) denote the PWM counter, maximum value of the counter and PWM cycle, respectively; \(v_{\text{A}}\) is the instantaneous phase A voltage; \(CR_{\text{2m}}\) is the upper limit for \(CR_{2}\). The duty cycle depends on the amplitude relationship between the  
Figure 8: AHBC with three multiplexed current sensors for a five-phase SRM.  
Figure 9: Block diagram of soft current chopping control.  
PWM counter and the corresponding comparison value. Current sampling is synchronized with the counter overflow. In the ECR, the upper limit \(CR_{2\text{m}}\) should be smaller than \(M_{\text{CTR}}\) and is chosen such that the lower switch can be fully turned on at an overflow of the PWM counter in the case that a very large negative average voltage is required to be applied.