esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 2 Related Works
### Visual Feedback for Low-Vision and Blind Users
#### 2.3.1 Enhanced Visual Representation for Visually Impaired Assistance  
Specific implementations include displaying enhanced obstacle images through head-mounted devices [69; 71] or converting environmental depth information into high-contrast colors [77] and multi-level brightness variations [28; 70]. Experimental evaluations demonstrate that these visual feedback methods have achieved positive results in information transmission. For instance, [PERSON] et al. designed a sign-reading system that enhances the visual salience of indoor text (e.g., room numbers), enabling visually impaired users to accurately perceive and recognize this information [74]. In stair navigation research, researchers projected visually highlighted cues directly onto stair surfaces, and visually impaired participants not only perceived these visual feedback cues but also reported a stronger sense of safety [75]. Similarly, [PERSON] et al. developed a navigation system that used high-contrast visual markers to indicate directions, with validation results showing that visually impaired users could effectively perceive and interpret these navigation cues [73]. [PERSON] et al. explored the feasibility of LED light-based assistance for visually impaired individuals by installing LED strips along the edges of obstacles to enhance edge visibility. Their findings indicated that this approach helped visually impaired users better perceive and avoid obstacles [78]. These studies collectively demonstrate the feasibility of visual feedback as a medium for information transmission, showing that visually impaired individuals can accurately perceive and understand optimized visual cues.
## 3 Methods
### A2C2f-FRFN
#### 3.1.2 Structure of A2C2f-FRFN  
Among the architectural enhancements in YOLOv12, the Residual Efficient Layer Aggregation Network (R-ELAN) represents a significant innovation. Departing from conventional designs, this module introduces a residual shortcut connection spanning from input to output with an additional scaling factor (default value 0.01). The architecture first employs transition layers to adjust channel dimensions, generating unified feature maps. These processed features subsequently undergo multi-submodule transformations before concatenation with original features, forming an optimized bottleneck structure that substantially enhances feature representation effectiveness. This architecture is integrated with the Area Attention mechanism to create a novel composite module (denoted as A2C2f in YOLOv12's official implementation), replacing the legacy C3k2 modules from previous YOLO iterations.  
Notably, the ABlock module housing Area Attention employs a Multi-Layer Perceptron (MLP) for feature transformation. In YOLOv12's implementation, the channel expansion ratio was strategically reduced from 4 to 1.2 (2 for N-/S-/M-scale models) to optimize computational resource allocation. While this modification improves operational efficiency, it risks compromising attention effectiveness through excessive parameter simplification. To address this limitation, we propose replacing the original MLP structure with a Feature-Refinement Feedback Network (FRFN). This substitution enhances both feature extraction capability and redundant information elimination, thereby fully realizing the potential of Area Attention. The enhanced architecture (ABlock-FRFN) subsequently forms the basis for our proposed A2C2f-FRFN module as a superior alternative to the original A2C2f configuration.  
The A2C2f-FRFN module adeptly synergizes the spatial feature representation enhancement capabilities inherent in the original A2C2f structure with the distinct advantages of FRFN in refining features and suppressing redundancy. This synergy enables the module to dynamically enhance spatial feature representations while effectively curtailing information redundancy. The architecture of A2C2f-FRFN is illustrated in Figure 1.  
Figure 3: Structure of Feature-Refinement Feedforward Network(FRFN).
# Advances in Random Fiber Lasers and Their Sensing Application
## 3 Control of Output Characteristics
### Wavelength  
Research on the wavelength of the RFL involves tunability and multi-wavelength, as the wavelength of the RFL can design flexibly.  
Tunable modules are fundamental structures to achieve tunable RFLs. The continuous tunable random laser was realized from 1553.9 nm to 1565.4 nm in 2013 [106], by combining SMF and multi-channel wavelength-tunable elements: F-P cavity and Mach-Zehnder interferometer formed by long-period gratings. The experimental setup for the tunable multi-wavelength RFL is illustrated in Figure 12. In 2014, a tunable erbium-doped RFL based on the random distributed feedback of backward Rayleigh scattering went up to 40 nm (1525-1565 nm) of the range of tunable wavelength, by connecting 20-km-long SMF and tunable fiber F-P interferometer filter [83]. In 2017, broadband tunable single-mode RFL with a range of more than 1500-1570 nm was realized, having adopted a tunable F-P filter [107]. These performances open the further prospect for applications in sensing and optical communication.  
Also, multi-wavelength lasers have potential applications in optical fiber sensing. RFLs with multi-wavelength can be realized by adding an array of FBGs, Sagnac loop filter, Lyot filter, as well as cascaded Brillouin conversion. In 2017, [PERSON] et al. proposed a narrow-line-width dual-wavelength RFL based on semiconductor optical amplifier (SOA) and FBGs [108]. In 2018, an erbium-doped RFL was achieved with stable multi-wavelength. The experimental setup of multi-wavelength erbium-doped RFL is depicted in Figure 13. Moreover, the experimental results showed that 24 stable laser lines can be generated in the range of 3 dB flatness when the pump power is 350 mW (Figure 14) [109]. In 2020, multi-wavelength (19 laser lines) laser output with flat amplitude was obtained by using a tunable incoherent optical pump and Lyot filter [110].  
Figure 12: Experimental setup for the tunable multi-wavelength RFL.  
Figure 13: Experimental setup of multi-wavelength erbium-doped RFL. ISO, isolator; HNLF, high nonlinear optical fiber.
## 4 Computer Simulation Results
### Frequency Conversion to Radiation with the Wavelength 4 \(\mu\)m
#### 4.1.2 Incident LFW's Intensity Is Equal to Zero  
Below, we briefly discuss peculiarities of the frequency down-conversion if the incident LFW intensity is equal to zero (\(|A_{10}|^{2}=0\)), and the intensity of the incident IFW is non-zero (\(|A_{20}|^{2}=0.4\), for instance). In this case, at \(\gamma=1\), a much longer distance (\(z=80\)) of the pulses' interaction is required for the LFW generation. Obviously, the required distance can be decreased by the incident intensity increasing. For example, at \(\gamma=2\), the distance decreases five times, but the frequency down-conversion occurs in the same way as at \(\gamma=1\): there are no improvements in the pulse shapes.  
The main feature of the interactions of the pulses involve developing their instability, which destroys the pulse shapes. Therefore, this scheme involving the pulse interactions at chosen parameters cannot be effectively used.  
As above, a sign of the phase mismatching \(\Delta_{21}k\) remarkably influences the frequency down-conversion process, but the pulse shapes do not remain smooth. If \(\Delta_{21}k\) is positive, then the IFW energy converts partially to the LFW's one. The conversion efficiency of the frequency down-conversion is about 24%. If the phase mismatching between LFW and IFW is negative, then the LFW generation is weaker than in the previous case (\(\eta_{1}=0.12\)), and the IFW amplification occurs.  
It should be noted that, in contrast to the frequency conversion in the crystal AgGaS\({}_{2}\), for the tellurium crystal, we observe a completely different situation, as one can see below.
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 4 Security Analysis
### Informal Security Analysis
#### 4.1.1 Providing Mutual Authentication  
In our proposed protocol, both participants \(\mathcal{U}_{k}\) and \(\mathcal{CSP}\) mutually authenticate each other to ensure their legitimacy. \(\mathcal{CSP}\) authenticates \(\mathcal{U}_{k}\) by checking whether \(auth_{k}\stackrel{{?}}{{=}}h(ID_{k}\|ID_{CSP}\|X_{k}\|r_{4})\) holds or not. Any adversary cannot pass this check, as he cannot compute valid \(X_{k}=h(ID_{k}\|x)\), because it involves the secret key \(x\) of \(\mathcal{CSP}\). So, \(\mathcal{CSP}\) only authenticates the legitimate user \(\mathcal{U}_{k}\). Likewise, \(\mathcal{U}_{k}\) authenticates \(\mathcal{CSP}\) while checking \(auth_{csp1}^{\prime}\stackrel{{?}}{{=}}h(ID_{k}\|ID_{CSP}\|x_{k}\| sk_{k-f-csp}\|r_{4}\|r_{7})\). The valid \(\mathcal{CSP}\) can pass this check because an adversary cannot know the value \(ID_{CSP}\) of the legitimate \(\mathcal{CSP}\). Furthermore, an adversary cannot compute \(X_{k}\) because he does not have any knowledge of server's secret key. Hence, the proposed protocol provides mutual authentication among \(\mathcal{U}_{k}\) and \(\mathcal{CSP}\).
## 2 Background
### Decoder  
The BCH decoders can be categorized as hard decision and soft decision decoders [15; 16]. These decoders'realization can be broadly classified into three categories: Central Processing Unit (CPU) [4], Very Large Scale implementation (VLSI) [17], and GPU implementation [8]. Various hardware implementations of BCH decoders were discussed by [PERSON] [7]. BCH decoders can be categorized by the place of the decoders. The decoders can be either located on-chip within memory device [18] or outside the memory device [19]. The focus of this paper is on the decoder being outside the memory device.  
The syndrome generator is the first step of the BCH decoding process [6; 20]. The syndromes \(S_{i}\) of the received vector \(r(x)\) are given as:  
\[S_{i}=r(a^{i}) \tag{3}\]  
In other words, the syndrome generator checks if the received code vector \(r(x)=r_{n-1}x^{n-1}+...+r_{1}+r_{0}\) has the roots as \(a^{1}\), \(a^{2}\),..., \(a^{2t}\). If so, then there are no errors in the received code vector. In the case of an error, the key-equation solver and the error locator steps are executed. For \(t\) bit error correction on a narrow sense BCH code, it is sufficient to find \(t\) syndromes, because the elements of a conjugacy class have the same minimal polynomial \(\phi_{i}(x)\). We have discussed an alternate approach to share the syndrome generator and encoders in [14]; Figure 1 depicts the area sharing between the encoder and the syndrome generator presented in [14]. This method requires separate error protection to the parity bits, and one proposal is to use a Single-Level Cell (SLC) for the parity bits to reduce error probability.  
An error locator polynomial \(\Lambda(x)\), which has dependency on the error location, gives a hint about the error location, and it is given by the equation:  
\[\Lambda(x)=\sum_{i=0}^{t}\Lambda_{i}x^{i}=(1-X_{1}x)(1-X_{2}x)...(1-X_{t}x) \tag{4}\]  
where \(X_{i}\) represents the error location of the vector \(r(x)\). The key equation:  
\[S(x).\Lambda(x)=\Omega(x)\;mod\;2t \tag{5}\]  
shows the relationship between the error locator polynomial and the error evaluator polynomial; moreover, Newton identities [3] show the relation between the error locator polynomial \(\Lambda(x)\) and the syndromes \(S_{i}\). There have been many algorithms like Berlekamp-Massey (BM), Peterson, and others proposed to solve the key equation [3; 7], but the inversion-less BM (iBM) algorithm is predominantly used in high throughput architectures [6; 21]. [PERSON] et al. [22] proposed a novel folded method to reduce the area in the hardware architecture, but the proposed method takes more clock cycles and is proportional to the folding factor. For the final step, the Chien Search (CS) algorithm is used to locate the error position from the error locator polynomial equation. [PERSON] et al. proposed a low power and high throughput parallel CS algorithm in [23].
## 4 Evaluation Results
### Grout-Injection Material Physical Property Change Measurement Comparison Results
#### 4.2.1 Acrylic-Resin Grout-Injection Material  
The physical property changes of the three different acrylic resin grout injection specimens were mutually compared. The results are shown that the three acrylic resin products are all relatively susceptible to physical property changes under the effect of water flow erosion and substrate behavioral stress. Product C, in particular, showed a higher stability than the other two products with regards to the degradation factors. Refer to the below radar chart in Figure 13.
## 3 Results
### Temporal Variation  
For monthly averages, the seasonal variation in NO\({}_{2}\) concentration was very similar for all types of areas. The highest monthly average NO\({}_{2}\) concentrations occur from October to February, while the lowest in June and July (Figures 5 and 6). The difference between the lowest and highest mean value is usually almost 50%. It should be noted that monthly threshold values were not introduced. Therefore, when it comes to monthly variation, we relate these values to the annual average. The 40 \(\upmu\)mol/m\({}^{2}\) level is exceeded from the months of February to October in all types of reception areas (Figure 5). During the summer season, from June to August, for all types of areas, the average is less than 40 \(\upmu\)mol/m\({}^{2}\). The temporal distribution of NO\({}_{2}\) pollution is primarily due to the changes in emissions associated with the burning of fossil fuels. The data for January 2019, due to weather conditions making it difficult to acquire a large number of good quality satellite scenes, are less reliable.  
In April, the number of national parks with a monthly average \(>\)40 \(\upmu\)mol/m\({}^{2}\) was about 50% lower than in February and October, and a similar relationship occurred for tourist regions. In contrast, for landscape parks and the health resorts, the differences were smaller. In autumn and winter, the value of 40 \(\upmu\)mol/m\({}^{2}\) is exceeded for more than 50% of tourist reception areas (except for national parks) (Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reception** & **February 2019** & **April 2019** & **July 2019** & **October 2019** \\
**Areas** & **February 2019** & **April 2019** & **July 2019** & **October 2019** \\ \hline National parks & 48\% & 21\% & 0\% & 43\% \\ Landscape parks & 77\% & 50\% & 2\% & 82\% \\ Tourist regions & 54\% & 28\% & 0\% & 71\% \\ Health resorts & 71\% & 42\% & 0\% & 55\% \\ Provincial capitals & 88\% & 94\% & 23\% & 100\% \\ \hline \end{tabular}
\end{table}
Table 2: Share of reception areas with NO\({}_{2}\) concentration above 40 \(\upmu\)mol/m\({}^{2}\).  
Figure 5: Mean monthly NO\({}_{2}\) concentrations in the specific types of areas: A—national parks, B—landscape parks, C—tourist regions, D—health resorts, and E—provincial capitals.
## Materials and Methods
### The Experimental Setup and Procedures  
The samples used in experiments are 6000-series Al-Mg-Si alloy plates with the dimension of 38 mm \(\times\) 20 mm \(\times\) 3 mm. They were cut from an aluminum alloy block (Al6061-T6). The roughness and flatness of samples were standardized by keeping the same machining conditions. The heat-treatment processes applied to the aluminum plates was presented schematically in Figure 1 of Reference [56]. Before inducing thermal aging to the specimens, they were solution heat-treated for 4 h at 540 \({}^{\circ}\)C and then water-cooled quickly to put preexisting precipitating microscopic particles back into the solution, trapping the precipitates in the solution and yielding a quasi-homogeneous material state. Each sample was then heat-treated at 220 \({}^{\circ}\)C, for different heat-treatment times of 0 min, 20 min, 40 min, 1 h, 2 h, 10 h, 100 h and 1000 h. As a result, these samples were prepared with gradually increasing levels of thermal aging.  
Figure 1: Diagrammatic sketch of the processes taking place when laser irradiates the liquid layer constrained by a transparent solid.  
For experimental evaluation of the acoustic nonlinearity of thermally aged aluminum alloys, it is essential to nondestructively generate and detect finite-amplitude acoustic waves. The experimental configuration is depicted schematically in Figure 2. During the experimental process, plate-type samples were oriented horizontally and were mounted on a translation stage to adjust the laser generating position in the x-y plane. The transient stresses in the thin LSCP layer was generated by using a pulsed laser (max output power of 4 J) at the wavelength of 1064 nm, with pulse duration of 10 ns. A combination of a half wave plate and a polarized beam splitter (PBS) was implemented to tune the output laser energy. A transparent glass plate placed before the convex lens was used to reflect a portion of laser energy to the energy meter. The laser energy irradiating directly on the top surface of the tested structure was monitored in real-time through the measured ratio between the energy transmitted to the sample surface and that reflected to the energy meter. The diameter of the generation laser spot was \(\sim\)3.0 mm. The out-of-plane displacement at the bottom surface was detected by an OFV-5000 interferometer (Polytec, Waldbronn, Germany, 2007), produced by Polytec, operating at 633 nm optical wavelength. The frequency range where the interferometer can be used varies from near DC to 20 MHz. A 1064 nm filter is placed in front of the interferometer to eliminate the influence of the generation laser. The detection laser was focused on the bottom surface to a diameter of approximately 80 \(\upmu\)m to 100 \(\upmu\)m. Finally, detected signals were displayed on a RIGOL DS4024 digital storage oscilloscope.
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 2 IberSpeech-RTVE 2022 Evaluation Databases
### The Basque Parliament Dataset
#### 2.5.1 Audio Data  
The Basque Parliament audio data were stored in 16 kHz 16-bit signed single-channel PCM WAV files. Audio recordings were originally made through the BP audio system (desktop microphones), thus they are generally clear with high signal-to-noise ratios. Two different audio files (each approximately one hour long) were provided for development and testing, respectively. Both audios were extracted from the same plenary session, which featured speech samples from several (not many) speakers, who may switch from Spanish to Basque (or vice versa) during their turns. Speaker turn changes and voting events were both managed by the president of the BP and involved a certain amount of silent or slightly noisy regions, but speaker turn overlaps were very uncommon.
## 4 Analysis of the Influence of Overlying Strata Movement on the Stability of Coal
### Stress Concentration of Coal along the Goaf  
It can be seen from the stress curves (Figure 4) that the stress below the coal seam increased with the mining of the working face, and the closer to the goaf, the faster the stress value increased. Among them, the stress fluctuation phenomenon at the 400 s position was caused by the breakage and rotation of the roof during the advancing process of the working face. After the mining was completed, the stress value at the No. 1 position tended to decrease, and the stress values at other positions tended to stabilize.  
Figure 8 shows the distribution law of lateral support pressure at marking points 1 and 2 after the mining of the coal seam in the working face. It can be seen that after the mining, the vertical stress at the position 0-4 m away from the goaf sharply increased, from 6.9 MPa to about 14.1 MPa, and vertical stress in this position reached the peak value. The vertical stress of the section from 28 m to 44 m tended to be stable, which is the original rock stress region. The analysis showed that the stress balance of the original strata was destroyed by the mining of the working face, resulting in the single-hump shape of the vertical stress, which increased at first and then decreased. The stress concentration phenomenon was formed near the edge of the goaf.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Coordinates** & \(u\) **Field of Point** & \(u\) **Field of Point** & \(v\) **Field of Point** & \(v\) **Field of Point** \\
**(m)** & **1 (cm)** & **2 (cm)** & **1 (cm)** & **2 (cm)** \\ \hline (10,30) & 0 & \(-13.1\) & 16.8 & \(19.1\) \\ (20,30) & 0.2 & \(-16.4\) & \(11.1\) & \(11.8\) \\ (30,30) & \(-1.6\) & \(-18.0\) & 8.5 & \(8.7\) \\ (40,30) & \(-2.  
After mining, the equilibrium state of the original stress field of the rock stratum was broken, and the stress of the overlying strata in the goaf was transferred to the coal seam along the goaf side, which made the stress concentration phenomenon appear in the edge coal of the goaf. At the edge, the coal reached the peak strength, a plastic zone appeared, the bearing capacity gradually decreased, and the vertical stress presented a single-hump shape that first increased and then decreased.
## 4 Demodulation Method
### Low Complexity Demodulation Methods
#### 4.2.3 SED Method  
Since the CSI of received preamble and the CSI of received data were complex numbers, other methods required complex multiplication when calculating the Euclidean distance. Therefore, multiplications in the demodulation process were necessary for each sub-carrier and antenna. Because the multiplication required more instruction clock cycles than the addition, it greatly affected the complexity of the calculation. We proposed a way to compare similarity using only the magnitude difference, not the complex Euclidean distance to solve this problem. A similar Euclidean distance (\(q_{l,m}\)) can be denoted as  
\[q_{l,m}=\ \sum\
olimits_{j=1}^{N}\sum\
olimits_{c=0}^{C-1}\Big{|}Re\Big{\{}p_{ j,l}(c)\Big{\}}-Re\Big{\{}d_{j,m}(c)\Big{\}}\Big{|}+\ \Big{|}Im\Big{\{}p_{j,l}(c)\Big{\}}-Im\Big{\{}d_{j,m}(c)\Big{\}}\Big{|}. \tag{20}\]  
Because additional operations and multiplication were not required to calculate \(q_{l,m}\), the SED method had a lower computational complexity than other methods. Data were demodulated to the smallest value among the similar Euclidean distance between the preamble and the data symbol throughthe Equation (12). But precisely a carrier was demodulated, and symbols were decided/estimated during the demodulation process.