esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Sea Trial  
A sea trial was performed in the shallow waters area the South China Sea to evaluate the capabilities of PF-SCFDE. The sea depth of the test area was 208 m. As shown in Figure 9, the MSK UWA communication signal was transmitted using an acoustic transducer. The source level was divided into three grades, which are 196, 190, and 184 dB, respectively. The depth was approximately 80 m. The receiving signal was collected by a self-contained hydrophone located at a depth of 97 m, and the distance was approximately 5.3 km.  
The sound velocity profile measured is shown in Figure 10. The velocity is close to constant in the upper layer of the water up to 100 m, and it has a negative gradient distribution in the lower layer. A total of 210 groups of signals were transmitted in the sea trial. In addition, a measurement of the channel is made by sending linear frequency modulation signals periodically in the experiment. Channels measured in a certain period are shown in Figure 11. The maximum channel delay was approximately 18 ms. The channels can be divided into four clusters. It is shown that the channel has a certain time variability, but the main path energy was relatively stable.  
Figure 10: Measured sound velocity.  
Figure 9: Sea trial program.  
The center frequency of the transmitted MSK signal was 6 kHz, the transducer bandwidth was 4-8 kHz, the data communication rate was 2 kbps, and the transmission information sequence per frame was 256 bits. The received MSK signal was processed by SCFDE and PF-SCFDE, respectively.  
Note that due to the signal transmitted in different times, the UWA channel changed during the sea trial, hence there was a significant difference in the received signal SNR. Table 3 shows the number of error bits in the differently received SNRs. It is shown that the number of error bits for PF-SCFDE and SCFDE reduces as the SNR increases, and the overall number of error bits for PF-SCFDE is smaller than that of SCFDE. In accordance with the different SNR intervals in the table, the error bits of PF-SCFDE are reduced by 33, 16, and 2. Based on the statistics of the 210 groups of received symbols of 5.376 \(\times\) 10\({}^{4}\) bits, the number of bit errors of SCFDE and PF-SCFDE was 278 and 227, respectively. PF-SCFDE outperformed SCFDE by 18.35%. The received signal is prone to phase shift due to the errors caused by the signal synchronization and Doppler frequency shift. In addition, PF-SCFDE is more sensitive to the phase shift due to the imaginary domain information is applied in the process.
# Single-Mode Polymer Ridge Waveguide Integration of Organic Thin-Film Laser
## 2 The Polymeric Single-Mode Waveguide  
Optical simulations using the finite difference eigenmode solver support the single-mode characteristic. Figure 2 shows the cross section of the single-mode ridge waveguide structure as  
Figure 1: (**a**) Schematic sketch of the OLAS integrated into the polymeric single-mode waveguide; and (**b**) the structural formula of Alq\({}_{3}\) and DCM2.  
described in the introduction as well as the TE0 and TE1 mode distribution with air (refractive index \(\approx\)1.0002 @650 nm) and EpoClad as the surrounding cladding medium.  
For the ridge-type waveguide with air as upper cladding, the TE0 mode is asymmetric and extends into the lower cladding (see Figure 2a). The effective refractive index was calculated to be 1.561 @650 nm. Figure 2b shows the TE0 mode distribution for the symmetrical case where the waveguide core is surrounded by the cladding material making it a buried waveguide. The mode field maximum is located in the waveguide core and the minority of the field intensity is located to be outside and is called evanescent field. In this structure, the decay length of the evanescent field is about 1 m. For the buried waveguide, the effective refractive index of the TE0 mode is 1.58 @650 nm. In both cases, higher modes such as TE1 (see Figure 2c,d) are not capable of propagation in the waveguide core.
## 3 Results and Discussion
### Impact of Network Dimension on Positioning Performance
While grids with two available stations were only sporadically distributed, grids with one or unavailable base stations were distributed at the edge of the underwater space, where the acoustic ranging signal of the subsurface buoy and the surface buoy could not reach. When the station distance increased from 7 km to 9 km, \(\text{EPG}_{\text{ASN}=0}\) and \(\text{EPG}_{\text{ASN}=2}\) decreased by 27.63% and 3.82%; \(\text{EPG}_{\text{ASN}=1}\), \(\text{EPG}_{\text{ASN}=3}\), and \(\text{EPG}_{\text{ASN}=4}\) increased by 19.40%, 45.83%, and 10.85%, respectively; while \(\text{EPG}_{\text{ASN}=5}\) decreased by 86.63%, which was primarily linked to the obvious variation in the PDOP. Based on the above analysis, for the \"3+1+1\" network configuration, the seafloor base station distance of 9-13 km and the subsurface buoy height of lower than 2.5 km were optimal in terms of positioning range and positioning accuracy. Considering Equation (4), the submersible positioning accuracy was less than 30 m within an underwater space of 25 km \(\times\) 25 km \(\times\) 3 km.
# A Class of Perfectly Secret Autonomous Low-Bit-Rate Voice Communication Systems
###### Abstract  
This paper presents an autonomous perfectly secure low-bit-rate voice communication system (APS-VCS) based on the mixed-excitation linear prediction voice coder (MELPe), Vernam cipher, and sequential key distillation (SKD) protocol by public discussion. An authenticated public channel can be selected in a wide range, from internet connections to specially leased radio channels. We found the source of common randomness between the locally synthesized speech signal at the transmitter and the reconstructed speech signal at the receiver side. To avoid information leakage about open input speech, the SKD protocol is not executed on the actual transmitted speech signal but on artificially synthesized speech obtained by random selection of the linear spectral pairs (LSP) parameters of the speech production model. Experimental verification of the proposed system was performed on the Vlatacom Personal Crypto Platform for Voice encryption (vPCP-V). Empirical measurements show that with an adequate selection of system parameters for voice transmission of 1.2 kb/s, a secret key rate (KR) of up to 8.8 kb/s can be achieved, with a negligible leakage rate (LR) and bit error rate (BER) of order \(10^{-3}\) for various communications channels, including GSM 3G and GSM VoLTE networks. At the same time, by ensuring perfect secrecy within symmetric encryption systems, it further highlights the importance of the symmetry principle in the field of information-theoretic security. To our knowledge, this is the first autonomous, perfectly secret system for low-bit-rate voice communication that does not require explicit prior generation and distribution of secret keys.  
speech security; perfect secrecy; vernam cypher; MELPe voicecoder; common randomness; sequencial key distillator; linear spectral pairs; linear prediction +
Footnote †: journal: Applied Optics and Photonics
# Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks
## 2 Materials and Methods  
### Related Concepts and Symbol Definition  
#### 2.1.1 Q-Matrix  
In cognitive diagnostic models (CDMs), a Q-matrix corresponding to test items is frequently utilized to infer a student's mastery of skills [16]. This procedure is similar to how a teacher observes students' responses and then analyzes their skill mastery depending on the concepts or skills tested in the test items. Therefore, the purpose of this work was to develop a cognitive diagnosis approach for neural networks based on Q-matrix restrictions. As shown in Equation (1), the Q-matrix explains how the \(K\) skills or attributes to be tested are used to create \(J\) test question items [17].  
\[\mathbf{Q}=\left(q_{jk}\right)_{j=1,2,\ldots,J,k=1,2,\ldots,K} \tag{1}\]  
where \(q_{jk}\in\{0,1\}\) and denotes whether the \(k\)th skill will be needed for the item \(j\). \(K\) denotes the number of skills, and \(J\) denotes the number of items. The values \(q_{jk}=1\) and \(q_{jk}=0\) denote the two scenarios in which skill \(k\) is examined and not examined in item \(j\), respectively. Each row of the Q-matrix is called a q-vector, e.g., \(\mathbf{q}_{j}=(q_{j1},q_{j2},\ldots,q_{jK})\), which represents the details of the skills examined in item \(j\). In an item, when the number of examined skills is more than two, there may exist interactive relationships between the skills, which are reflected by mutual promotion or inhibition among the skills.
###### Abstract  
In recent years, production of biohydrogen and biomethane (or a mixture of these; biohydthane) from organic wastes using two-stage bioreactor have been implemented by developing countries such as Germany, USA and the United Kingdom using the anaerobic digestion (AD) process. In Thailand, biohydthane production in a two-stage process has been widely studied. However, in Malaysia, treating organic and agricultural wastes using an integrated system of dark fermentation (DF) coupled with anaerobic digestion (AD) is scarce. For instance, in most oil palm mills, palm oil mill effluent (POME) is treated using a conventional open-ponding system or closed-digester tank for biogas capture. This paper reviewed relevant literature studies on treating POME and other organic wastes using integrated bioreactor implementing DF and/or AD process for biohydrogen and/or biomethane production. Although the number of papers that have been published in this area is increasing, a further review is needed to reveal current technology used and its benefits, especially in Malaysia, since Malaysia is the second-largest oil palm producer in the world.  
dark fermentation; anaerobic digestion; integrated system; biohydrogen; biomethane; POME
## 1 Introduction  
In recent years, the development of solution-processable organic field-effect transistors (OFETs) has attracted increasing interest owing to their widespread use in various printable electronic devices, including flexible displays, low-cost radio-frequency identification tags, and large-area sensors [1; 2]. It is well known that carrier transport in OFETs is sensitive to the ordering and packing of the organic semiconducting molecules interfaced with the gate insulator, and localized trap states caused by the disorder significantly affect the electrical performance of OFETs, such as field-effect mobility, threshold voltage, and subthreshold slope. It has been reported that molecular ordering in the organic semiconductor films of OFETs with bottom-gate configurations, which are conventionally used for OFET applications, depends largely on the surface properties, such as surface energy [3,4] and roughness [5], of the underlying gate insulators. Therefore, the development of reliable organic gate insulators is critical for improving the performance of bottom-gate OFETs. For the development of solution-processable OFETs, thermally curable polymer gate insulators, such as polyimide (PI) [6,7], poly(4-vinylphenol) (PVP) [8], and poly(methylsilsesquioxane) (PMSQ) [9, 10, 11, 12], have gained much attention because these materials allow the formation of desirable semiconductor/insulator interfaces with limited interfacial mixing.  
The localized trap states at the solution-processed organic semiconductor/polymer gate insulator interfaces have been studied by measuring the impedance characteristics of metal-insulator-semiconductor (MIS) capacitors [13, 14, 15, 16, 17, 18]. It has been reported that the impedance characteristics of organic MIS capacitors can be described with equivalent circuit models similar to those of Si-based metal-oxide-semiconductor (MOS) capacitors, which were suggested by [PERSON] and [PERSON] [19,20]; further, energetically distributed localized states are formed at the organic semiconductor/polymer gate insulator interfaces [14, 15, 16]. However, the influence of the polymer gate insulators on the interface states in organic MIS capacitors has not been ascertained in existing literature.  
In this paper, we report the determination of interface-state distributions in poly(3-hexylthiophene) (P3 HT)-based organic MIS capacitors with polymer gate insulators of PI, PVP, and PMSQ using impedance spectroscopy (IS). First, we discuss the influences of patterning and thermal annealing of the organic semiconductor films on the impedance characteristics of P3 HT MIS capacitors to determine the optimal fabrication process for our tested MIS devices. Next, we examine the applicability of three equivalent circuit models to the extraction of interface states from the conductance characteristics of the P3 HT MIS capacitors. Finally, we attempt to identify the correlation between the densities of interface states in the organic MIS capacitors and the electrical characteristics of OFETs.
# Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area
## 3 Results
### Evaluation Using Satellite-Derived Vegetation Products
#### 3.2.1 Evapotranspiration and GPP
As shown on the correlation difference map (right column of Figure 6), these improvements were mainly located on the northern part of the Black Sea and of the Caspian Sea, which are regions mainly covered by crops and grasslands. Some improvements in Western Europe were also visible for the evapotranspiration variable, which is also a region mainly covered by crops and grasslands. Blue areas indicate a degradation of the the correlation after the assimilation at northern latitudes over boreal forests and grasslands. It showed an inconsistency  
Figure 5: Monthly seasonal scores (RMSD and correlation) of the open-loop (blue) and analysis (red) simulations from 2007–2015 averaged over the Euro-Mediterranean area compared to the products of: **(a)** GLEAM Evapotranspiration (E) and **(b)** FLUXNET-MTE gross primary production (only 2007–2011). Red shade indicates an improvement from the assimilation process with either a lower error or a higher correlation score. between the assimilated satellite products and the GLEAM evapotranspiration and FLUXNET-MTE GPP products in these regions. Further investigations are needed to explain this inconsistency.
## 3 Results and Discussion
### Influence of Wetlands on Water Quality
#### 3.5.7 pH Assessment  
Regarding pH, it was observed that, in general, there is an increasing trend between the inlet and outlet of the Pantanal, but it remains close to neutrality, ranging between 7.0 and 7.5 (Figure 16).  
This trend towards alkalinity was observed by [PERSON] and [PERSON] [60] when comparing the contributions of geochemistry, microbial metabolism, and aquatic photosynthesis to the alkalinity of pH in ephemeral wetlands in southeastern Australia. In the study, the authors highlight that in that locality, the abundant growth of aquatic plants modifies the chemical composition of the water through the absorption of nutrients, such as calcium and magnesium, as well as the sequestration of CO\({}_{2}\) by the process of photosynthesis in the water column, increasing the carbonate alkalinity in the water. These nutrients absorbed by the plants are not available in the water column, so they do not form solid calcium and magnesium carbonates like calcite, which normally buffer the pH within a certain fixed range [60]. In the study conducted by [PERSON] [61] in the wetlands of Limuru, Kenya, the author identified low variation in pH at the inlet and outlet, also noting a slight trend of increasing alkalinity.  
The pH influences the mobility of phosphorus in the aquatic environment when in the presence of Al, Fe, Ca, and Mg ions, promoting its adsorption on soil particles, sediments, and the water column. Thus, the pH of the system and the concentration of these elements  
Figure 15: Spatial variation of the average Turbidity (NTU) at the monitoring stations of the Pantanal (IA07, IA14, IA15, IA16, IA17).  
determine which ion will be most active in P sequestration. In acidic soils, P in the inorganic form can be adsorbed on hydrated oxides of Fe and Al; in addition, P can precipitate as insoluble Fe phosphates. [54]. [PERSON] et al. [44] highlight that in the Pantanal, during the \"decoada\" period, the DO concentration varies between 5.0 and 2.0 mg/L due to the decomposition of dead organic matter; as a consequence of this, soluble CO2 is released (favoring acidification). During the study period, pH values outside the range of 6 to 9, allowed for Class 2, were not identified. Although a pattern of reduction in DO was identified in the wetlands of the Miranda River, it was observed that the average DO values were above 5.0 mg/L.
## 2 Background and Related Work
### Background
#### 2.1.1 Academic and Administrative Chatbots  
The use of chatbots in academic and administrative domains within Arabic higher education institutions is relatively new but is rapidly expanding, focusing on localizing chatbot technology to meet students' linguistic and cultural needs [24; 25]. Arabic's diverse dialects, complicated word structures, and cultural nuances make chatbot development difficult. To overcome these obstacles, sophisticated natural language processing, adaptive algorithms, and culturally sensitive frameworks for localized and efficient communication are needed [26].  
Notwithstanding these obstacles, notable progress has been made [27]. Using chatbots for academic and administrative tasks is a growing trend in Arabic higher education institutions [24], designed to autonomously handle repetitive operations, such as admission requests, course registration, and responses to frequently asked questions, using the local dialect.  
Jooka [25], a bilingual assistant that enhances university admissions, and Nabiha [28], a chatbot that offers academic counseling in the Saudi dialect, are implementations that demonstrate the potential of Arabic chatbots to streamline administrative processes and improve access to education.  
However, significant gaps remain in the understanding of the efficacy of these tools, particularly regarding student engagement and their practical applicability in academic settings. This underscores the necessity for thorough evaluation systems to measure their impact.
## 2 Methods and Modeling
### Model Parameters  
Detailed descriptions of measured SOFC, including the lanthanum strontium cobalt ferrite (LSCF) cathode, Ni-scandia stabilized zirconia (Ni-SSZ) active (or support) anode layer, and electrolyte bilayer, can be found in Ref. [10]. The physical parameters are adopted from the literature, or fitted to match the desired characteristics of measured SOFC. Table 2 shows the list of input parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** & **Reference** \\ \hline Pressure & \(p\) & 1.0 & bar & [10] \\ \hline Anode thickness & \(d_{\text{a}}\) & 20 & \(\mu\)m & [10] \\ \hline Cathode thickness & \(d_{\text{c}}\) & 20 & \(\mu\)m & [10] \\ \hline Electrolyte thickness & \(d_{\text{e}}\) & 10 & \(\mu\)m & [10] \\ \hline Anode electronic conductivity & \(\sigma_{\text{s,a}}\) & 4800 & S m\({}^{-1}\) & [15] \\ \hline Cathode electronic conductivity & \(\sigma_{\text{s,c}}\) & 1600 & S m\({}^{-1}\) & [15] \\ \hline Anode ionic conductivity & \(\sigma_{\text{el,a}}\) & 2.0 & S m\({}^{-1}\) & [29] \\ \hline Cathode ionic conductivity & \(\sigma_{\text{el,c}}\) & 2.0 & S m\({}^{-1}\) & [29] \\ \hline Electrolyte ionic conductivity & \(\sigma_{\text{el,e}}\) & 2.0 & S m\({}^{-1}\) & [29] \\ \hline Anode exchange current density & \(i_{0,\text{a}}\) & 1200 & A m\({}^{-2}\) & [10] \\ \hline Cathode exchange current density & \(i_{0,c}\) & 2200 & A m\({}^{-2}\) & [10] \\ \hline Surface exchange coefficient & \(k_{\text{surf}}\) & \(2.0\times 10^{-5}\) & m s\({}^{-1}\) & [17] \\ \hline Concentration of oxygen ions & \(C_{\text{O}^{2}}\). & \(8.3\times 10^{4}\) & mol m\({}^{-3}\) & [17] \\ \hline Chemical diffusion coefficient & \(D_{\text{chem}}\) & \(4.4\times 10^{-10}\) & m\({}^{2}\) s\({}^{-1}\) & [17] \\ \hline Series resistance of anode support layer & \(R_{\text{s,a}}\) & 0.5 & \(\Omega\) cm\({}^{2}\) & [10] \\ \hline Double layer capacitance of anode & \(C_{\text{dl,a}}\) & 0.5 & F cm\({}^{-2}\) & [10] \\ \hline Molar fraction of hydrogen in fuel & \(x_{0,\text{H}_{2}}\) & 0.970 & / & [10] \\ \hline Molar fraction of water in fuel & \(x_{0,\text{H}_{2}}\)O & 0.030 & / & [10] \\ \hline Molar fraction of oxygen in air & \(x_{0,\text{O}_{2}}\) & 0.207 & / & [10] \\ \hline Molar fraction of nitrogen in air & \(x_{0,\text{N}_{2}}\) & 0.793 & / & [10] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input parameters of the SOFC model.
## 3 Predicting the Signal Attenuation Level at Free Space (Section A)
### Prediction Experiment for 2.4-72 GHz  
We introduced the LSTM-ML process and compared the expected rain and fog attenuation levels to the accurate attenuation results executed over a broad spectrum of signal strengths, from 2.4 GHz to 72 GHz. The experiment began with training the LSTM system using the 913 sample records collected. Figure 5 depicts the results achieved during the ML training process.  
The top chart presents the trained attenuation levels derived from the training data. The bottom graph depicts the actual attenuation levels collected by the satellite. At this stage, we assumed that the LSTM-ML was appropriately calibrated and ready for the prediction process. We executed the same experiment with the same sampled data but with different signal strengths of 2.4 GHz, 10 GHz, 23 GHz, 48 GHz, and 72 GHz. Figure 9a-e depict the results of the experiments performed for each of the five frequencies. The  
Figure 4: Comparison of the actual and predicted signal for one month. Reprinted with permission from ref. [1]. Copyright 2021 IEEE.  
blue and brick lines represent the actual and predicted attenuation levels. The charts below depict the differences between the two charts. We can see the significant similarity between the two graphs. We can also see that all frames demonstrated similar maps with neglected differences.  
Figure 5: The attenuation levels derived from the training data compared to actual data. Reprinted with permission from ref. [1]. Copyright 2021 IEEE.  
Figure 6: Cont.  
Figure 6: _Cont._