esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimenting the Automatic Recognition of Non-Conventionalized Units in Sign Language
## 1 Introduction
### Limitations of the Current Acceptation of Continuous Sign Language Recognition  
In the previous discussion, we show that the current acceptation of CSLR is what we refer to as CLexSR, which is the recognition of lexical sign glosses within continuous signing. On the basis of strong linguistic arguments, it appears that this direction is strongly biased, and will not make it possible to go towards SLU and _a fortiori_ to SLT. Indeed, the _gloss sequence_ description misses main SL characteristics: the multilinearity, which makes it possible to convey several types of information at once; the prevalent use of space, which structures SL discourse; the iconicity, which enables us to show while saying.  
Our point is hardly new, and has been put forward early on by a few researchers in the field of SLR. For instance, [16] insisted on the importance of space as a grammar component of French Sign Language (LSF). In another work, [37] mentioned complementary arguments, observing that the focus had been on conventional signs--_gestures_-leaving out the grammar of SL--primarily referring to the iconic characteristic of SLs, and the multilinear aspects of SL, like facial expression or body posture.  
Related to the fact that CLexSR has been the main concern of researchers, leaving out the three linguistic characteristics we just mentioned, specific types of SL corpora have become popular. Many of them consist of artificial elicited sentences, repeated several times, with eliciting material--and annotation schemes--consisting of sequences of glosses. This is, for instance, the case of the SIGNUM Database and the CSLR100 corpus. Probably the most popular corpus, RWTH-PW is more spontaneous although the interpreted SL lacks generalizability and the topic--weather forecasts--is quite restricted.  
Another limitation of using glosses as the training objective of SLR systems that should be noted, is the fact that glosses do not necessarily represent the meaning of signs they are associated to. This is highlighted by [6], warning that _\"used alone like this, glosses almost invariably distort face-to-face SL data\"_.  
Independently, we appreciate that a few leads have been initiated towards different directions than CLexSR. SLT is one of them, although it has been mostly driven by gloss supervision, on the limitative RWTH-PW corpus. On the other hand, focusing on SLT or on the recognition of lexical signs only, with _black box_ architectures, may prevent developments in the linguistic description and automatic analysis of SL. A few approaches have actually chosen to deal with linguistic matters, yet on very small corpora or only superficially.  
In the next section, we propose a broader and more relevant acceptation of CSLR that deals with the aforementioned issues.
# Detecting and Mitigating Attacks on GPS Devices
## 4 Factors Contributing to GPS-Denied or GPS-Disrupted Environments
### GPS Spoofing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**Difficulty of**} & \multirow{2}{*}{**Type of System**} & **RF Hardware** & **Signal Generation** \\  & & **Implementation** & & **Platform** & **Environment** \\ \hline [PERSON] et al. & [62] & Low & SDR & HackRF One & GPS-SDR-SIM GPS \\ [PERSON] et al. & [63] & Low & SDR & BladeRF x40 & GPS-SDR-SIM GPS \\ [PERSON] et al. & [50] & Low & SDR & BladeRF X40 & GPS-SDR-SIM GPS \\ [PERSON] et al. & [64] & Low & SDR & HackRF One & GPS-SDR-SIM GPS \\ [PERSON] and [PERSON] & [51] & Low & SDR-based & Unknown & GNU Radio \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of low-cost GPS spoofer system implementations. with which GPS timing information can be spoofed, such as when the GPS receiver has an accurate position but the attacker controls the absolute time information [66].
## 2 Literature Review of Network Intrusion Detection
### Challenges in Traditional Intrusion Detection Systems  
The effectiveness of traditional intrusion detection systems is limited by several factors, including the ever-evolving nature of cyber threats, the complexity of network traffic data, and the high rates of false positives and negatives [38]. The dynamic nature of cyber threats presents a major challenge for intrusion detection systems. Attackers continuously develop new techniques and strategies to exploit vulnerabilities in network systems, making signature-based methods less effective in detecting new attacks. This highlights the need for more adaptive and flexible intrusion detection systems that can learn to identify emerging patterns and threats.  
In addition, the complexity of network traffic data poses a significant challenge for intrusion detection systems. Network traffic data is often high-dimensional, noisy, and diverse, which makes it difficult for traditional IDS to effectively analyze and identify patterns of malicious activity. Moreover, manual feature engineering, which involvesselecting relevant features and reducing the dimensionality of input data, can be time-consuming and may not always capture the complex relationships and patterns within network traffic. Another major challenge for traditional intrusion detection systems is the high rate of false positives and negatives. False positives occur when the IDS incorrectly flags normal network traffic as malicious, while false negatives happen when the IDS fails to detect a genuine attack. Both issues can result in increased operational overhead and diminished efficiency in network security management.
## 3 Results
### Characterization of the Coated Scaffolds
#### 3.1.2 Surface Roughness  
The surface roughness was determined for the coated collagen scaffolds and the pure PCL scaffolds. For this purpose, five scaffolds each were examined at five different locations. The PCL scaffolds without coating showed a roughness of 4.11 \(\pm\) 0.27 \(\upmu\)m and the collagen-coated samples a roughness of 3.35 \(\pm\) 0.3 \(\upmu\)m (see Figure 2).
## 3 Price Predictions Using Deep Learning Models
### Time-Series Forecasting Models
#### 3.2.6 Neural Hierarchical Interpolation for Time-Series Forecasting (N-HiTS)  
Neural Hierarchical Interpolation for Time-Series Forecasting (N-HiTS) stands as a significant advancement in the field, aiming to improve both the accuracy and computational efficiency of predictions. Building upon the foundational N-BEATS framework, N-HiTS introduces a novel hierarchical approach that integrates multirate sampling techniques with multiscale synthesis strategies. This innovative architecture addresses two key challenges in long-range time-series forecasting: computational complexity and prediction volatility [44].  
The core of N-HiTS lies in its hierarchical construction. Each model comprises multiple stacks, each housing several blocks. These blocks leverage Multi-Layer Perceptrons (MLPs) to predict coefficients for both backcasting and forecasting tasks associated with their assigned basis functions. Backcasting serves to refine subsequent inputs, while the collective forecast outputs from all blocks are ultimately aggregated to generate the final prediction. Each stack within the architecture specializes in capturing distinct data attributes by utilizing unique sets of basis functions, offering increased flexibility and adaptability to varying data characteristics.  
The hierarchical approach in N-HiTS manifests in two key aspects:  
* Multirate sampling: Blocks closer to the input receive data at a higher sampling rate, enabling them to capture fine-grained details and higher frequency components. Deeper blocks receive downsampled data, focusing on larger trends and lower frequencies. This hierarchical sampling promotes computational efficiency by allocating resources where they are most beneficial.
* Multiscale synthesis: Predictions from each block are synthesized at different scales depending on their level within the hierarchy. High-frequency components are reconstructed first, followed by the gradual incorporation of lower frequency information. This progressive approach helps to mitigate volatility in long-range forecasts, offering more stable and reliable predictions.  
By integrating these hierarchical strategies, N-HiTS achieves a balance between efficiency and accuracy. Compared to its predecessor N-BEATS, N-HiTS demonstrates improvements in both computational speed and long-range forecast quality, making it a valuable tool for real-world applications where both precision and efficiency are critical.
# Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System
## 4 Experimental Verification
### Comparison of TR Algorithms  
Figure 15 and Table 7 are comparisons of the three TR algorithms of NT, WTNT, EKM, and EIASC. EKM and EIASC apply the principle of iterative optimization to find the optimal solution, which is more complicated than the WTNT algorithm. From the response results, under the same parameters, both NT, WTNT, and EKM can ensure the stability of the system, but EKM will bring greater jitter, and the values of VAR and ITAE are large and unstable, while EIASC diverges the step response and does not apply to this experimental platform. Comparing NT and WTNT, we can see that the WTNT algorithm has a faster response speed and higher steady-state accuracy. Meanwhile, the VAR and ITAE values of WTNT are smaller than NT, indicating that the WTNT algorithm has stronger robustness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **TS** & **ESS** & **VAR** & **J(ITAE)** \\ \hline
**T2-1** & 0.0548 & 0.0005 & 1.2038 & 974.5 \\
**T2-2** & 0.0496 & 0.0096 & 1.4572 & 885.1 \\
**T2-3** & 0.0598 & 0.0081 & 1.1244 & 1105.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Three experiments comparison in IT2 FLC mode.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **TS** & **ESS** & **VAR** & **J(ITAE)** \\ \hline
**CP-1** & 0.1046 & 0.0049 & 3.0578 & 1164.6 \\
**CP-2** & 0.1426 & 1.0235 & 1.0406 & 1090.9 \\
**CP-3** & 0.1358 & 0.3083 & 1.6174 & 1251.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Three experiments comparison in Cp mode.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **TS** & **ESS** & **VAR** & **J(ITAE)** \\ \hline
**CP-1** & 0.1046 & 0.0049 & 3.0578 & 1164.6 \\
**CP-2** & 0.1426 & 1.0235 & 1.0406 & 1090.9 \\
**CP-3** & 0.1358 & 0.3083 & 1.6174 & 1251.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Three experiments comparison in T1 FLC mode.  
On the whole, for this experimental platform, the WTNT algorithm results are the best, and the algorithm itself does not require iteration, the calculation speed is the fastest, and it is more suitable for real-time control systems.
## 3 Preliminaries: Network Model and Problem Definition
### Network Information Synchronization Protocol  
At the user layer, users periodically report their own location coordinates to the edge nodes. After receiving the information, the node regards itself connected to the user and records the location information. At the edge layer, each edge node sends synchronous data to all adjacent nodes, so the node can obtain the topology of the edge layer and construct a routing table after receiving the data and store the status information of the remaining nodes in its own database. In the cloud layer, cloud server periodically sends synchronization information to all edge nodes to confirm link connectivity. As shown in Figure 3, all synchronization information is sent periodically. If synchronization is not received after a period of timeout, the node considers that it has disconnected from the device and does not retransmit.
# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
###### Abstract  
This study investigates the spatiotemporal dynamics of land surface temperature (LST) across five distinct land use/land cover (LULLC) classes through high-resolution unmanned aerial vehicle (UAV) thermal remote sensing. Thermal orthomosaics were systematically captured at four diurnal periods (morning, afternoon, evening, and midnight) over an urban university campus environment. Using stratified random sampling in each class with spatial controls to minimize autocorrelation, we quantified thermal signatures across bare soil, buildings, grassland, paved roads, and water bodies. Statistical analyses incorporating outlier management via the Interquartile Range (IQR) method, spatial autocorrelation assessment using [PERSON]'s \(I\), correlation testing, and Geographically Weighted Regression (GWR) revealed substantial thermal variability across LULLC classes, with temperature differentials of up to 17.7 \({}^{\circ}\)C between grassland (20.57 \(\pm\) 5.13 \({}^{\circ}\)C) and water bodies (7.10 \(\pm\) 1.25 \({}^{\circ}\)C) during afternoon periods. The [PERSON]'s \(I\) analysis indicated notable spatial dependence in land surface temperature, justifying the use of GWR to model these spatial patterns. Impervious surfaces demonstrated pronounced heat retention capabilities, with paved roads maintaining elevated temperatures into evening (13.18 \(\pm\) 3.49 \({}^{\circ}\)C) and midnight (2.25 \(\pm\) 1.51 \({}^{\circ}\)C) periods despite ambient cooling. Water bodies exhibited exceptional thermal stability (SD range: 0.79-2.85 \({}^{\circ}\)C across all periods), while grasslands showed efficient nocturnal cooling (\(\Delta\)T = 23.02 \({}^{\circ}\)C from afternoon to midnight). GWR models identified spatially heterogeneous relationships between LST patterns and LULC distribution, with water bodies exerting the strongest localized cooling influence (\(R^{2}\approx\) 0.62-0.68 during morning/evening periods). The findings demonstrate that surface material properties significantly modulate diurnal heat flux dynamics, with human-made surfaces contributing to prolonged thermal loading. This research advances urban microclimate monitoring methodologies by integrating high-resolution UAV thermal imagery with robust statistical frameworks, providing empirically-grounded insights for climate-adaptive urban planning and heat mitigation strategies. Future work should incorporate multi-seasonal observations, in situ validation instrumentation, and integration with human thermal comfort indices.  
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## 6 Conclusions  
This work is a significant achievement by experimentally demonstrating a data transmission record of 50 Gbps per \(\lambda\) with a silica microsphere WGMR-based Kerr-OFC light source. In this experiment, we have designed and demonstrated the silica microsphere WGMR-based Kerr-OFC built in-house and used as a light source for data center interconnects (DCIs). The Kerr-OFC consists of several carriers spaced 400 GHz apart together with powerful pre- and post-linear equalization techniques, such as a linear symbol-spaced adaptive decision-feedback equalizer (DFE) with feed-forward (FF), and feedback (FB) taps provide an alternative to ensure low-cost and low-complexity IM/DD schemes for the transmission of NRZ-OOK modulated signals over 2 km SMF links. The obtained results show that pre- and post-equalization techniques allow overcoming the ISI and help recover the signal from distortions caused by limited bandwidth, enabling higher data-rate alternatives to intra-DCIs. Without post-equalization, the received 40 Gbaud and 50 Gbaud NRZ-OOK signals are below the 7% HD-FEC limit for Kerr-OFC generated carriers (\(-1\)), (0), and (+1). The linear post-equalization, namely 33-FF and 15-FB taps, improves BER performance for carrier (+1), which allows us to achieve the BER floor below the 7% HD-FEC limit even for NRZ-OOK signals at 60 Gbaud.  
Not applicable.  
This research was funded by the European Regional Development Fund project No. 1.1.1.1/18/A/155 \"Development of optical frequency comb generator based on a whispering gallery mode microresonator and its applications in telecommunications\". The taper fiber preparation was supported by the European Social Fund within project number No. 8.2.2.0/20/I/008 and by the the Austrian Science Fund FWF within the DK-ALM W1259-N27. The development of the program for the optical fusion splicer was funded by the Mega-grant of the Ministry of Science and Higher Education of the Russian Federation, Contract No. 075-15-2021-633. The fabrication of test samples of microspheres was funded by the Russian Science Foundation, Grant No. 20-72-10188. Communication Technologies Research Center, Riga Technical University (RTU) acknowledges support from RTU Science Support Fund. The authors declare no conflict of interest.
## 8 Technical Challenges and Potential Solutions
### Heterogenous Systems Design Modeling Issues  
The accumulation of tests to establish a system of codes and standards, particularly dynamic effects and fire performance, encountered problems in the data collection, space limitation, and a high cost. It is, therefore, vital at this stage to anticipate the exact numerical modeling details on the stability and safety of PFISBs. Detailed 3D solid and shell element modeling can accurately predict the nonlinearity in material properties and geometry. Still, multi-story buildings are always considered impractical because a high computational efficiency is needed. For this reason, detailed modeling is replaced by being simplified, such as with a 1D beam, wire, or line elements with nonlinear springs. On the other hand, due to unique detailing, high-rise PFISBs involves a varying internal and external IMC stiffness at each story. Without considering the stiffness of every joint, _P_-\(\Delta\)-\(\delta\) effects, and geometrical and material nonlinearities during stability, robustness, and diaphragm investigations, a simplified model may result in an inaccuracy. However, due to the problems highlightedabove, recently developed simpler models can only mimic the elastic behavior and cannot simulate the plastic behavior, making them unreliable in creating codes.  
Moreover, as the authors recommended adopting advanced materials such as steel composite sections with a high performance and lightweight concrete, composite, and FRP/GFR panels, the interactions between the heterogeneous materials will become complex. However, an additional study should be conducted to produce more precise and efficient ways to design simplified modeling for capturing each of the aspects mentioned above while evaluating the structural performance.
# Guided Facial Skin Color Correction
## 3 Proposed Method
### Skin Color Extraction  
This step roughly extracts the skin color region near the face. We use a common approach using clustering in the color space and morphological labeling in the image space:  
1. The color of each pixel is classified by the color distribution of the entire image in the HSV color space. Each pixel is assigned the label of the cluster to which it belongs.
2. Some regions are generated by concatenating neighboring pixels with the same labels in the image space. Regions mainly in the detected face area \(\Omega_{\mathrm{rect}}\) (Equation (2)) are extracted, and regarded as the facial skin region.  
As for the method for clustering color distributions, since the skin color in the detected face area (Section 3.1) is known, simpler approaches, e.g., _k-means_ method [17] may give satisfactory results. We use _k-means++_[18] to select stable seed clusters.  
We use the above _k-means_ clustering [18] in this paper. Additionally, for robustness to _value_ (luminance) and _saturation_ variation and also simplicity, we only use the _hue_ values for clustering the 1D distribution. The aforementioned procedures (i) and (ii) are performed to detect the face region \(\Omega_{S}^{\textit{hue}}\). Then, at each pixel contained in the region \(\Omega_{S}^{\textit{hue}}\)  
Figure 2: Face detection. (**a**) original image; (**b**) detected face area (candidate rectangles); and (**c**) face area in the original image.  
the _saturation_ and _value_ components are thresholded. The condition for the skin color at pixel \(p\) is experimentally defined as follows, and the skin color region \(\Omega_{S}\) is obtained:  
\[\Omega_{S}:=\Big{\{}p\ |\ p\!\in\!\Omega_{S}^{\textit{Inve}},\ s_{p}\!\in\![ \widehat{s}\!-\!0.2,\widehat{s}\!+\!0.2],\ v_{p}\!\in\![0.15,0.95]\Big{\}}, \tag{3}\]  
where \(\widehat{s}\) is the median of the _saturation_ in the face area. The _saturation_ and the _value_ are normalized to be in the range \([0,1]\) and the threshold values are set to excellently extract the facial skin color region on our dataset, which is available on the project page. The number of clusters depends on the photographic environment, especially if the skin color region needs to be perfectly extracted to the edge of the face because the edge color tends to change due to light reflection. However, the post-processing can handle this even in the case of rough extraction. We experimentally set \(k=4\) in this paper.  
For our guide image filtering, we define \(\Omega_{B}\) and \(\Omega_{\partial S}\) using the dilation function, which is a type of morphological operation. They are given as  
\[\begin{split}\Omega_{S}^{\prime}&:=\text{ dilation}(\Omega_{S}),\\ \Omega_{B}&:=\overline{\Omega}^{\prime}S,\\ \Omega_{\partial S}&:=\Omega_{S}^{\prime}\setminus \Omega_{S},\end{split} \tag{4}\]  
where \(\text{ dilation}(\cdot)\) is the dilation function with a structuring element, which consists of a circle with a 20 pixel radius. The overline \(\widehat{\cdot}\) is the complement and the operator \(\setminus\) is the set difference. An example of each region can be seen in Figure 3.
## 3 The Proposed Protocol
### Attacker Model  
With the increasing trend of adversarial communication, attackers can interfere with UAV communication by means of channel listening and message replay in response to the highly open nature of UAV communication links. This scenario is characterized by  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
the Dolev-Yao attacker model [29], which is commonly used in formal analysis of security protocols, as \"the network is under the control of the attacker\". Therefore, attackers in this scenario are considered to have similar network attack capabilities to attackers in the Dolev-Yao attacker model, as defined below.  
* The ability to listen to, intercept and store all messages passing through the UAV network, including authentication and communication information.
* The ability to decrypt intercepted authentication messages and to use the key parameters obtained to forge authentication and communication messages.
* The ability to establish a link to the UAV via a proxy node and send constructed or intercepted authentication and communication messages to participate in the authentication and communication between the UAV and the GCS.
# HS-SPME Combined with GC-MS/O to Analyze the Flavor of Strong Aroma Baijiu _Daqu_
###### Abstract  
_Daqu_ has gained wide attention because it is an essential source of microorganisms and flavor in baijiu production. In this study, HS-SPME combined with GC-MS/O was used to analyze the volatile flavor components of Strong aroma baijiu _Daqu_. DI-GC-O was used to choose the best extraction fiber to extract the representative overall aroma profile of _Daqu_. A total of 139 compounds were identified in the six different maturity stages of _Daqu_, and these compounds are of different types and concentrations. HS-SPME combined with GC-MS/O was used to analyze the aroma active substances in the finished _Daqu_, and a total of 43 aroma compounds were identified. The OAVs of 21 aromatic compounds were calculated based on the quantitative analysis results of MHS-SPME. Eighteen compounds with OAVs \(\geq\) 1 made significant contributions to the overall aroma of _Daqu_, including guaiacol, 4-ethyl-2-methoxy phenol, 2-ethyl-3,5-dimethylplyrazine, etc.  
baijiu_; _Daqu_; MHS-SPME; DI-GC-O; volatile flavor components