esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optical Properties  
Perovskite CsGeI\({}_{3}\) can be widely used in solar cells materials, so it is particularly important to study its optical properties. The optical properties of solids can be described by the dielectric function \(\varepsilon(\omega)=\varepsilon_{1}(\omega)+i\varepsilon_{2}(\omega)\). The dielectric function consists of intraband and interband transitions. Intraband transitions mainly occur in metals and interband transitions mainly occur in semiconductors. Interband transitions consist of direct and indirect transitions. Since the indirect transition only involves phonon dispersions and has little influence on the dielectric function, it can be ignored.
## 3 System Configuration
### Open Path Detection without Retroreflectors  
The requirements of non-fixed open-path gas detection, e.g., the atmospheric environmental monitoring, leak detection, security early warning, etc., promoted the standoff remote sensing. We focus on the open-path-averaged gas concentrations by the backscatter light from a remote hard target or topographic target. Other open-path systems that are deployed as point samplers or long-path with retroreflectors are beyond the scope of this review.  
In order to realize standoff gas detection, a number of laser-based techniques are available, such as TDLAS [34; 35; 137; 138], PAS [139], differential absorption lidar (DIAL) [140], CLaDS [141], and more recently active coherent laser spectrometers (ACLaS) [36]. The basic architectures of these techniques are shown in Figure 2. The OPL is often variable and unknown; therefore, performance is often quoted in a similar fashion to that of open path gas detectors, using the pathlength-integrated unit of ppm\(\cdot\)m [142]. The detection limits for such systems commonly range from sub ppm\(\cdot\)m to several hundred ppm\(\cdot\)m [35; 36; 141], typically over distances of open path from several meters to hundreds of meters or even kilometers. The performance of this system is typically limited by the level of received laser power, which is dependent on the incident laser power, distance between receiver and scattering target, type of scattering materials, and size of the receiver aperture [142; 143; 144]. High-power and broadband tuning lasers, such as OPO and EC-QCLs, are desired to achieve a higher signal-to-noise ratio (SNR) [145; 146; 147]. However, high-sensitive and low-noise detectors are particularly important for applications in public areas where eye safety should be considered.  
Recently, standoff trace detection has achieved tremendous progress and been applied in leaks [35], environmental monitoring [141], explosives [148; 149], combustion [147], unmanned aerial vehicles (UAV) [150; 151], and many other promising applications as well.  
Figure 2: Diagrams of the basic architectures of standoff gas detection techniques with non-cooperators: (a) TDLAS [35], (b) DIAL [149], (c) PAS [139], (d) ACLaS [36].
## 5 Experimentation, Results, and Analysis
### Analysis
#### 5.4.2 Qualitative Analysis
Handling of Ambiguity  
DCAT has shown the ability to resolve ambiguous scenes effectively. For instance, in an image of a basketball player from the Flickr8k dataset (Table 7), DCAT clearly states that the player is \"jumping and holding the basketball over the net\", providing context that is often missing in other baseline models. In the MSCOCO dataset (Table 9), DCAT offers a vivid description of a \"red stop sign sitting next to a country road\", providing clarity and specificity where other models might simply state \"a stop sign\". In the MSCOCO dataset (Table 12), while other models ambiguously describe a dog as \"running in the field\" or \"with a toy in mouth\", DCAT provides a vivid context by stating the dog is \"dashing through the grass, skillfully clutching a frisbee in its mouth, embodying the essence of play\". The qualitative analysis and human evaluation affirm the superiority of DCAT in generating high-quality captions that are both linguistically accurate and contextually rich, thereby validating the quantitative results.
## 2 Materials  
### Geo-KOmpsat-2a (Gkza)  
The GK2A is equipped with an Advanced Meteorological Imager (AMI). This holds sixteen channels comprising four channel categories, including visible channels, near-infrared channels, mid-wave infrared channels, and long-wave infrared channels [24]. The wavelength of the channels ranges from 0.431 \(\upmu\)m to 13.39 \(\upmu\)m, and the spatial resolution of each channel is 0.5 km, 1.0 km, and 2.0 km, depending on the channel (Table 1). It is possible to classify the observation data of the GK2A/AMI into three data types, depending on the spatial coverage: full-disk (FD), extended local area (ELA), and local area (LA) data. The temporal resolution of the channels changes depending on the data type; the FD data are observed every 10 min, and the others are observed every 2 min. Given its high spatial and temporal resolution, it is possible for the GK2A/AMI to monitor the SSI more frequently and accurately than COMS/MI and other low Earth orbit satellites [25]. In order to produce the matchup database with in situ measurements and train the ML model, in this study we used LA data observing the region around Korea.
# Positioning Methods and the Use of Location and Activity Data in Forests
## 6 Emerging Research Needs
### Landscape-Scale Mapping of Vegetation and Canopy Impacts on Position Accuracy  
Forest vegetation has repeatedly been shown to markedly diminish GNSS positioning accuracy due to multipath errors [229; 230; 231; 232; 233; 48]. Vegetation and topography also degrade local transmissions sent by Bluetooth or other radio-based technologies [235]. To advance the integration of real-time positioning systems for critical operations in natural resources, new approaches to quantify the impacts of vegetation and topography systematically over large landscapes are necessary. For instance, baseline, multipath errors for GNSS-based positioning and RSSI loss for conventional frequencies (VHF, UHF, 900 MHz) could be mapped across expansive areas using remote sensing satellite or airborne LiDAR data. These predicted errors could then be integrated into end-user PNT applications to aid mission planning for operational and tactical support.
## 4 Structure of SFrame Video Conferencing System
### Analysis of SFrame  
The double encryption method applied by SFrame is as follows [3]:  
* Hop-by-Hop Encryption (DTLS-SRTP)
* End-to-End Encryption (Symmetric)  
Figure 2 illustrates the process by which the communication parties, [PERSON] and [PERSON], securely transmit media using the SFrame protocol with double encryption. First, the two entities share a group symmetric key schedule through a key management system with E2 EE security in advance.  
[PERSON] retrieves the SFrame symmetric key from the pre-shared key schedule and utilizes it to encrypt the media. Next, the encrypted media are packetized before being transmitted to the SFU. [PERSON] encrypts the packetized media with the Datagram Transport Layer Security for Secure Real-time Transport Protocol (DTLS-SRTP) symmetric key and transmits it to the SFU. The SFU decrypts the media packet using the pre-shared DTLS-SRTP symmetric key with [PERSON], and then re-encrypts the decrypted media with [PERSON]'s DTLS-SRTP symmetric key before delivering it. Finally, [PERSON] can decrypt the ciphertext sequentially with a DTLS-SRTP key and an SFrame symmetric key. Consequently, the plaintext of the media is not exposed to the SFU. The SFU checks only the media metadata and routes the media packets received from [PERSON] to [PERSON].  
The symmetric key used for double encryption in the encryption/decryption process of the SFrame protocol is the same, and a key management system for the E2 EE function is essential for symmetric key sharing. The _index_ and _base_key_ information related to the \(KID\) are assigned to the entities of the group belonging to the key management system. Therefore, each entity obtains the _base_key_ information used for decryption by checking the \(KID\) of the transmitted media header. For SFrame encryption, _base_key_ is used as an input to the HKDF function to derive the SFrame symmetric key and \(salt\) value. Subsequently, a _nonce_ is generated by XOR \(salt\) with \(ctr\), and the _nonce_ value is used for encryption/decryption.
## 2 Materials and Methods
### Methods
#### 2.3.2. Separation of Spatial Climate Factors  
In complex ecological scenarios of mining areas, the TVDI is influenced by multiple driving factors such as temperature, precipitation, and human activities. To analyze the independent effect of mining activities on the TVDI, this study proposes the TVDI-CFS method to isolate the impact of mining activities. The GTWR model was employed to separate the influence of climate factors by decomposing temperature and precipitation across different temporal and spatial scales, thereby providing a more precise understanding of the impact of mining activities on the TVDI. The study area is relatively small, and on the spatial scale, temperature and precipitation can be approximated as homogeneous, while they exhibit variability on the temporal scale. The specific steps are as follows: (1) using the annual TVDI, temperature, and precipitation data from the coal mining-free period (2000-2015), a quantitative relationship between temperature, precipitation, and the TVDI was established based on the GTWR [45; 46; 47] model. The GTWR model is shown in Equation (9). (2) The trained quantitative model was used to predict the TVDI under coal mining-free conditions by inputting the temperature and precipitation data from 2016 to 2021. (3) The predicted TVDI was compared with the actual TVDI values obtained from remote sensing monitoring, and the difference is considered as the TVDI variation caused by coal mining activities after removing the influence of temperature and precipitation. This variation is defined as TVDI-CFS. Further analysis of the impact of mining activities on the TVDI was conducted using the TVDI-CFS.  
\[y_{i}=\beta_{0}(u_{i},v_{i},t_{i})T_{i}+\beta_{1}(u_{i},v_{i},t_{i})P_{i}+ \varepsilon_{i}(i=1,2,\ldots,n) \tag{9}\]  
where \(y_{i}\) denotes the TVDI at the \(i\)-th spatiotemporal location, \(T\) is the temperature (in \({}^{\circ}\)C), \(P\) is precipitation (in mm), \(\beta(u_{i},v_{i},t_{i})\) is the coefficient of each independent variable at the \((u_{i},v_{i})\) location at time \(t_{i}\), n is the total number of samples, and \(\varepsilon_{i}\) is the error term.  
The TVDI-CFS model was used to predict the annual TVDI of the study area under no mining activity conditions, which was then compared with the actual TVDI derived from Landsat imagery (i.e., the TVDI influenced by coal mining activities). Through this comparison, the impact of coal mining activities on the TVDI was extracted, and the TVDI-CFS data for mining disturbances from 2016 to 2021 were used to analyze theDisturbance Boundary of TVDI-CFS from the open-pit mine and the spatial cumulative effects between two open-pit mines. Using the 2021 TVDI-CFS data as an example (Figure 3), the disturbance caused by mining activities on the TVDI was demonstrated.
###### Abstract  
The Mexican deaf community primarily uses Mexican Sign Language (MSL) for communication, but significant barriers arise when interacting with hearing individuals unfamiliar with the language. Learning MSL requires a substantial commitment of at least 18 months, which is often impractical for many hearing people. To address this gap, we present an MSL-to-Spanish translation system that facilitates communication through a spelling-based approach, enabling deaf individuals to convey any idea while simplifying the AI's task by limiting the number of signs to be recognized. Unlike previous systems that focus exclusively on static signs for individual letters, our solution incorporates dynamic signs, such as \"k\", \"rr\", and \"ll\", to better capture the nuances of MSL and enhance expressiveness. The proposed Hybrid Neural Network-based algorithm integrates these dynamic elements effectively, achieving an F1 score of 90.91%, precision of 91.25%, recall of 91.05%, and accuracy of 91.09% in the extended alphabet classification. These results demonstrate the system's potential to improve accessibility and inclusivity for the Mexican deaf community.  
M 2024 14 10186 19 10.3390/app142210186
# Timing Performance Testing and Regularity Analysis of eLoran System
## 2 eLoran Timing Error
### Authorization Performance Assessment Metrics
#### 2.2.3 Timing Accuracy  
The average timing deviation values monitored during the valid data period indicate the central tendency of the eLoran timing system in broadcasting the national standard time [24; 25]:  
\[\overline{\tau}=\frac{\sum\limits_{i=1}^{n}\tau_{i}}{n}, \tag{1}\]  
where \(n\) indicates the number of data points in the valid data period, \(\tau_{i}\) indicates the monitoring value of the timing deviation, and \(\overline{\tau}\) indicates the average value of the timing deviation in the valid data period.
# Production of Starch Esters by Roasting Potato Starch with Unripe Apple Juice
## 1 Introduction
The leading global producers of apples are China and the European Union, with an annual production of nearly 13 million tons in 2022 recorded for the latter [12]. In turn, Poland is the undisputed leader in apple production among the EU Member States. Over half of the apples produced in Poland are intended for processing. Ca. 90% of these include industrial apples, i.e., those of different varieties and with different degrees of ripeness, which are used for apple juice production [13]. For this reason, apple juice differs throughout the processing campaign in terms of active acidity, total acid content, and percentage share of individual acids depending on the production date. The increase in the total acid content, mainly malic acid, is related to the use of mostly unripeples for juice production [14]. The content of malic acid increases during the growth of young fruits, reaching the maximum value, and then decreases during their further growth and ripening [15]. A decrease in its content is mainly due to its consumption as an important substrate in the respiration process [15]. In addition, the malic acid content of ripening fruits decreases as a result of many transformations, including gluconeogenesis resulting in the production of sugars, tricarboxylic acids cycle (TCA), alcoholic fermentation, and the formation of complex secondary compounds, like anthocyanins and flavonoids [16]. The scientific literature widely describes the properties of starch esters produced using organic acids, but the novelty in this respect is the use of natural sources of these acids, like fruit juices. Apple juice is a natural source of malic acid, which, when appropriate technologies are used, can offer a cheap and safe raw material for the production of esterified starch. In our previous study, we used apple distillery wastewater to produce starch esters, while the aims of this work were to produce potato starch esters by roasting starch with unripe apple juice concentrate at different temperatures and to analyze the properties of the modified preparations.
## 2 Materials and Methods
### Statistical Analysis  
First, a one-way repeated-measures ANOVA was conducted on behavioral performance (accuracy) to evaluate the NR effects, followed by post hoc analyses to investigate whether NR algorithms used in the present study significantly changed the accuracy. Then, Pearson correlation analysis was conducted to investigate if the neural SNR and subjective noise-tolerance ratings correlated with accuracy changes. The neural SNR was calculated for each individual using leave-one-out grand averages of ERP temporal envelopes obtained from jackknife analysis, while individual subjective ratings were calculated by averaging scores across ten items from the Weinstein Questionnaire. Further, the relationship between the neural SNR and subjective ratings was evaluated using Pearson correlation coefficients. Lastly, in the case of both neural SNR and subjective ratings showing significant correlations with changes in accuracy, a multiple linear regression model was constructed incorporating both predictors in a stepwise manner, given that cortical and subjective measures may reflect different perspectives of individual noise tolerance.
# Earthquake Damage Index and Fragility Analysis of Steel Damper for Seismic Isolation Bridge
## 4 Fragility Analysis of Steel Dampers in Bridges Designed for Seismic and Isolation
### Result Analysis  
Figure 12 shows the fragility curves of the steel dampers installed in the longitudinal direction of the bridge, where the slight damage state is the primary performance state under the design earthquake. Taking the steel damper on the #2 pier as an example, at PGA = 0.15 g, the probability of moderate or higher damage (\(D\geq 0.206\)) occurring in the steel damper is zero. Even under higher seismic actions (PGA = 0.4 g), the probability of moderate or higher damage occurring is only 14.5%. The probability of experiencing serious or higher damage is 11.5%, and the probability of complete destruction is 4%. At PGA = 0.85, moderate or higher damage begins to dominate, with a probability of 52.5%, a probability of severe or higher damage of 41%, and a probability of complete destruction of 31%.  
Figure 13 shows the fragility curves of the steel dampers installed in the transverse direction of the bridge, which is similar to the situation in the longitudinal direction. Steel dampers installed along the transverse direction of the bridge also exhibit a high seismic performance reserve in the slight damage state. Taking the steel damper on the #2 pier as an example, at PGA = 0.15 g, the probability of moderate or higher damage (\(D\geq 0.206\)) occurring in the steel damper is zero. Even under higher seismic actions (PGA = 0.4 g), the probability of moderate or higher damage occurring is only 13.5%. The probability of experiencing serious or higher damage is 11.5%, and the probability of complete destruction is 6%. At PGA = 0.85, moderate or higher damage begins to dominate, with a probability of 51.5%, a probability of severe or higher damage of 43.5%, and a probability of complete destruction of 33%. In most seismic scenarios, the fatigue damage of the steel dampers is not a prominent issue.  
Figure 12: _Cont._
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 3 Problem Statement
### Path Establishment against Link Failures on SCION  
Since SCION forwarding paths are static, they collapse when one of the links fails. Link failures are handled by a three-pronged approach that typically masks link failures without any outage to the application and rapidly re-establishes fresh working paths [13]:  
* Beaconing periodically establishes new working paths.
* SCION control message protocol (SCMP) (SCION-equivalent of ICMP) is used for path-segment revocation. Failed links result in the rapid erasure of affected path segments from path servers.
* SCION end hosts use multi-path communication by default, thus masking link failures to an application with another working path. As multi-path communication can increase availability (even in environments with very limited path choice [10]), SCION beacon servers actively attempt to create disjoint paths, SCION path servers make an effort to select and announce disjoint paths, and end hosts compose path segments to achieve maximum resilience to path failure. Consequently, we expect that most link failures in SCION will be unnoticed by the application, unlike the frequent (although mostly brief) outages in the currently available Internet [38; 41].
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 2 Signal Model
### Complex Gaussian Mixture Model
For each batch, the parameters of the CGMM are updated by the following formulation [24]:  
\[\phi(l)^{(d)}=\frac{1}{M}{\rm tr}\bigg{\{}{\bf y}(l){\bf y}(l)^{H}\Big{[}{\rm R }_{c}^{(d)}\Big{]}^{-1}\bigg{\}} \tag{9}\]  
\[{\rm R}_{c}^{(d)}=\frac{{\rm A}_{c-1}^{(d)}}{{\rm A}_{c-1}^{(d)}+\sum\limits _{l\in l_{c}}\lambda(l)^{(d)}}{\rm R}_{c-1}^{(d)}+\frac{1}{{\rm A}_{c-1}^{(d)} +\sum\limits_{l\in l_{c}}\lambda(l)^{(d)}}\sum\limits_{l\in l_{c}}\lambda(l) ^{(d)}\frac{1}{\phi(l)^{(d)}}{\bf y}(l){\bf y}(l)^{H} \tag{10}\]  
where \({\rm A}_{c-1}^{(d)}\) denote the sum of the masks over all the previous frames, and it can be updated as follows:  
\[{\rm A}_{c}^{(d)}={\rm A}_{c-1}^{(d)}+\sum\limits_{l\in l_{c}}\lambda(l)^{(d)} \tag{11}\]  
It is worth noting that \({\rm R}_{c}^{(d)}\) is usually irreversible. In practical calculation, the inverse of \({\rm R}_{c}^{(d)}\) is usually replaced by a pseudo inverse. For every batch, the EM algorithm can be used to obtain the most optimal mask value for the current time-frequency bin only containing noise, and the covariance matrix in the case of the reverberation with noise or noise-only at each frequency bin can be estimated as follows [24]:  
\[{\rm M}_{c}^{(d)}=\frac{{\rm A}_{c-1}^{(d)}}{{\rm A}_{c-1}^{(d)}+\sum\limits _{l\in l_{c}}\lambda(l)^{(d)}}{\rm M}_{c-1}^{(d)}+\frac{1}{{\rm A}_{c-1}^{(d)} +\sum\limits_{l\in l_{c}}\lambda(l)^{(d)}}\sum\limits_{l\in l_{c}}\lambda(l)^ {(d)}{\bf y}(l){\bf y}(l)^{H} \tag{12}\]  
So, the covariance matrix in the case of reverberation can be given as follows:  
\[\mathbf{y}(l)=\mathbf{g}^{(d)}x_{\text{b}}^{(d)}(l) \tag{4}\]0  
Performing the following eigenvalue decomposition  
\[\mathbf{y}(l)=\mathbf{g}^{(d)}x_{\text{b}}^{(d)}(l) \tag{4}\]1where **P** is the matrix composed of the eigenvectors of the matrix \(\mathbf{M}_{\mathrm{c}}{}^{\left(\mathbf{x}+\mathbf{r}\right)}\), and \(\mathbf{\Lambda}\) is the diagonal matrix containing the eigenvalues, the eigenvector associated with the largest eigenvalue is extracted as the steering vector required for the MVDR beamforming. The coefficient vector \(\mathbf{w}_{\mathrm{b}}\) of the MVDR filter can be expressed as follows [24]:  
\[\mathbf{y}(l)=\mathbf{g}^{(d)}x_{\text{b}}^{(d)}(l) \tag{4}\]2  
In fact, the CGMM-MVDR is a beamforming-based method with blind estimation of the steering vector. The late reverberation leads to an inaccurate estimation of the steering vector. Since the blind estimation of the accurate steering vector from the reverberated speech with noise is a very difficult task, what we obtain here is the approximation of the steering vector. This does not mean that such approximations lead to false results, because the reverberation has been mostly removed by Kalman filtering rather than beamforming.