esa-sceva/satcom-chunk-collection huggingface dataset:
# One-Class Learning for AI-Generated Essay Detection
## 3 Results
The POS setting (see Figure 6) shows 327 (HBOS), 289 (ABOD), 276 (IsolationForest), 263 (LocalOutlierFactor), 249 (AutoEncoder), and 175 (OneClassSVM) incorrectly classified essays. Its performance falls between Text and Readability. For the Spanish dataset, the Text setting (see Figure 7) shows 384 (IsolationForest), 378 (LocalOutlierFactor), 370 (AutoEncoder), 357 (HBOS), 353 (ABOD), and 277 (OneClass SVM) incorrectly classified essays out of 700. These results are close to the random performance with this setting, with OneClassSVM being the best-performing method. The Repetitiveness setting (see Figure 8) highlights 358 (IsolationForest), 355 (ABOD), 355 (AutoEncoder), 354 (HBOS), 338 (LocalOutlierFactor), and 336 (OneClassSVM) incorrectly classified essays. Across all the methods in this setting, the performance is consistently poor, as indicated by the minimal deviation in the number of misclassified essays. The Emotional Semantics setting (see Figure 9) showcases 357 (IsolationForest), 357 (OneClassSVM), 351 (HBOS), 343 (ABOD), 339 (AutoEncoder), and 334 (LocalOutlierFactor) incorrectly classified essays. These results confirm that Emotional Semantics do not offer highly predictive information for the detection task, as shown in the English dataset. The Readability setting (see Figure 10) presents 355 (HBOS), 354 (ABOD), 352 (AutoEncoder), 267 (IsolationForest), 257 (LocalOutlierFactor), and 181 (OneClassSVM) incorrectly classified essays. This setting is more effective than others, as observed by performance metrics. In fact, OneClassSVM, LocalOutlierFactor, and IsolationForest are able to reach results that are above random performance. The POS setting (see Figure 11) shows 373 (AutoEncoder), 367 (IsolationForest), 356 (ABOD), 355 (HBOS), 352 (OneClassSVM), and 347 (LocalOutlierFactor) incorrectly classified essays, falling between Text and Readability. Overall, these results appear to be close to random performanc and in line with the results obtained in the Emotional Semantics, Repetitiveness, and Text settings for the English dataset. **RQ3:** The results obtained from the English (Table 3) and Spanish datasets (Table 4) reveal that there is a substantial difference in model performance. Considering the best outcomes obtained within each setting across all methods for English vs. Spanish essays, the following side-by-side F1-Score comparisons are obtained: Text (0.6694 vs. 0.5917), Repetitiveness (0.9553 vs. 0.5199), Emotional Semantics (0.6436 vs. 0.4881), Readability (0.8942 vs. 0.7238), and POS (0.7223 vs. 0.4968).
# A Reinforcement Learning Approach to Speech Coding
## 2 Current Standardized Speech Codecs  
The most widely installed speech codec today is the Wideband Adaptive Multirate (AMR-WB) speech codec [14]. It has been standardized for almost 20 years (since 2001, a narrowband version since 1999) and offers several transmitted bit rates from 6.6 kilobits/s (kbits/s) to 23.85 kbits/s that can be chosen depending on the channel quality. The one way algorithmic delay is 26 ms and it has been tested across many languages. The studies on neural networks for end-to-end speech coding thus far have used AMR-WB as a comparison codec.  
A newer speech codec is the Enhanced Voice Services (EVS) codec that was standardized by 3 GPP in September 2014. This codec offers enhanced quality for both narrowband and wideband speech, enhanced quality for mixed speech and music content, robustness to packet loss and delay jitter, and backward compatibility with AMR-WB [15]. The EVS codec was tested for 10 languages. At 9.6 kbits/s, EVS significantly outperforms AMR-WB at 23.85 kbits/s.  
Both of these successful codecs, and their predecessors in the past 35 years [16; 17], are based on taking in a frame or block of speech and using block-based least squares to calculate the coefficients of a linear prediction model. The best fixed excitation for the linear prediction model is then found from a sparse codebook in combination with an adaptive method to model the pitch excitation via analysis-by-synthesis (AbS) over the block of speech to minimize a perceptually weighted squared error. These parameters are then coded for transmission to the receiver to reconstruct the block of speech using a linear prediction model followed by post shaping the decoder output. The process is then repeated for the next block of input speech [18].
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 5 Trust Data Gathering
### Data Gathering  
**Direct gathering**. Almost all models use some form of direct trust. Direct trust is any trust metric gathered directly between trustor and trustee, without the use of a third-party node or entity. This can include analyzing the data sent in the communications, i.e., packets, communication frequency and the quality of the data [19], or using a system similar to a watchdog mechanism. In [20], a watchdog mechanism is used to monitor the routing, processing and data in nodes that are in direct communication with a given node. Many trust parameters can be gathered directly through this direct gathering and used in trust calculations later on, or to calculate a direct trust score.  
**Indirect gathering**. The opposite of direct gathering, indirect gathering is the method of using third-party nodes or entities to gather data and use these data to calculate indirect trust scores (also referred to as second-hand, recommended or reputation trust scores). This is a very common method of gathering data. Methods for selecting the nodes to gather this data include one-hop neighbors [16; 20; 38], nodes in the cluster [13; 14; 24; 29; 31], cluster head or base station feedback [22], multi-hop neighbors [26] and common neighbors between the trustor and trustee [25].  
**Broadcasting**. This is a method in which a node or cluster head broadcasts information. In [21], all communications are broadcasted from the beacon nodes so that other beacons can listen and check if the location information is correct when compared to their location table. In [35], the gateway information of a provider is broadcast and opened up for auction to the users, and, in [38], trust tables are multi-cast (it is multicast as it is not broadcasting to the entire network, but only its cluster) from the cluster head to its cluster after a table update has taken place. The broadcasting method removes a layer of privacy from the data being broadcast and prevents attacking nodes from fooling individual nodes, as all the nodes are able to check if a malicious node broadcasts inaccurate information relative to the other nodes in the group.
# A Review of Distribution Grid Consumption Strategies Containing Distributed Photovoltaics
## 3 Distribution Grid Consumption Strategy with Distributed PV
#### 3.1.3 Distributed PV Siting and Capacity Strategy
A voltage-based approach was proposed, and by analyzing the voltage reliability, it can be seen that when the power supply network is heavily loaded, the reliability indicators all improve with the increase in PV penetration, and conversely, when the power supply network is lightly loaded, the reliability indicators deteriorate instead. Italian researchers, on the other hand, in combination with their own water resources, have studied solar PV systems adapted to Mediterranean islands [51], using GIS to deal with the technical characteristics of each location and to calculate the potential PV capacity and the corresponding PV electricity production. In Sweden, some scholars are evaluating solar PV for rural electrification in the north of Ghana [52], concluding that PV is not the best option for the local population due to wiring problems that affect the economy. At this stage, the research of distributed photovoltaic siting and capacity is a broader topic involving a number of fields. Among them, the research on siting and capacity optimization based on intelligent algorithms is the most extensive and the most popular research direction. Through the introduction of intelligent algorithms, the process of distributed photovoltaic system access to the distribution network is optimized and controlled in order to find the optimal siting and capacity solution. In fact, based on mathematical modeling and simulation technology, siting and capacitation research can also be carried out, and the combination of various models and cross-research will be the focus of the development of this field in the future.
# A Polarization Independent Quasi-TEM Metamaterial Absorber for X and Ku Band Sensing Applications
## 1 Introduction
Those articles discussed the multiband operation, dense size, the various shape of the resonator with an irregular thickness of resistive sheets where absorption ranges from 50% to 90% approximately [22; 23; 24]. The recent trend to apply metamaterial absorbers in sensing applications represents a breakthrough in developing unique sensors for different engineering fields like biomedical applications, optics, chemical flow measurement, etc. Tunable metamaterial absorbers (TMAs) using a varactor diode were proposed for sensing applications where the dielectric constant is inversely proportional to the resonance frequency [25]. \(\Omega\)-shaped multimode resonators presented in [26] have six-bands with an average absorption rate over 97.21%, but no sensing applications yet. Numerical and experimental demonstrations were performed using metamaterial absorbers where the authors [27; 28] stated the sensing capability of material permittivity and pressure. A circular SRR-based metamaterial sensor was developed for microfluid, rotation and strain sensing but no application of absorption was mentioned [29]. Another researcher developed a microstrip metamaterial for liquid permittivity sensing [30] using a rectangular complementary-ring resonator (RCRR) but the Q factor of this unit is low. Single resonance is exhibited in a substrate-integrated waveguide (SIW) resonator where resonance frequency controlled dielectric loading and hence it cannot be independently tunable [31]. Herein, we propose a metamaterial absorber for pressure sensing applications in the X and Ku-band. The metamaterial absorber has been fabricated on a FR-4 (lossy) substrate (thickness \(t=1.575\) mm) which is a glass-reinforced NEMA-grade epoxy laminated material. These bands are used for satellite communications, especially tracking data relay satellite used for both the Space Shuttle and the International Space Station (ISS). Splitting the band into multiple segments varying by geographical region can be used for radio astronomy services, space research services, mobile satellite services, etc. The relative permittivity and permeability are \(-0.4213\) and \(-1.251\), respectively, at the highest absorption peak which is 99.6% (at frequency 13.78 GHz). This absorber was designed and simulated through the commercially available CST Microwave studio 2017 software. Details of the design procedure, and simulation steps are available in [32]. The design was intended to create a left-handed metamaterial (LHM) in X and Ku-band with maximum absorption and, hence, the reflection coefficient is also negative.
# A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring
## 4 Results
### State-of-the-Art Comparison
Our model achieves superior performance in terms of traditional evaluation metrics in most cases. Note that U-mAP and U-Recall are not calculated because all classes are known in the task 4. The bold represents the best performance on the metric. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Task IDs (\(\rightarrow\))** & \multicolumn{3}{c}{**Task 1**} & \multicolumn{3}{c}{**Task 2**} & \multicolumn{3}{c}{**Task 3**} \\ \hline  & WI-0.8 & UDR & UDP & WI-0.8 & UDR & UDP & WI-0.8 & UDR & UDP \\  & \begin{tabular}{c} (\(\downarrow\)) \\ \end{tabular} & \begin{tabular}{c} (\(\uparrow\)) \\ \end{tabular} & \begin{tabular}{c} (\(\uparrow\)) \\ \end{tabular} & \begin{tabular}{c} (\(\downarrow\)) \\ \end{tabular} & \begin{tabular}{c} (\(\uparrow\)) \\ \end{tabular} & \begin{tabular}{c} (\(\uparrow\)) \\ \end{tabular} & \begin{tabular}{c} (\(\downarrow\)) \\ \end{tabular} & \begin{tabular}{c} (\(\uparrow\)) \\ \end{tabular} & \
\begin{tabular}{c} (\(\uparrow\)) \\ \end{tabular} &
\begin{tabular}{c} (\(\uparrow\)) \\ \end{tabular} \\ \hline Faster RCNN [57] & 0.0645 & 17.58 & 0 & 0.0273 & 16.32 & 0 & 0.0164 & **24.69** & 0 \\ ORE [14] & **0.0528** & 18.58 & 31.28 & 0.0315 & 17.30 & 15.37 & 0.0209 & 23.67 & 14.95 \\ SA [52] & 0.0563 & 8.51 & 22.73 & **0.0181** & 5.74 & 13.83 & **0.0136** & 9.12 & 1.30 \\ \hline D-DETR [16] & 0.0600 & **20.74** & 0 & 0.0245 & 14.41 & 0 & 0.0187 & 34.48 & 0 \\ OW-DETR [17] & 0.0599 & 18.31 & 41.77 & 0.0319 & 16.24 & 35.88 & 0.0220 & 21.53 & 27.72 \\ \hline Ours & 0.0553 & 20.00 & **86.97** & 0.0251 & **19.35** & **69.89** & 0.0179 & 22.59 & **63.30** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: State-of-the-art comparison for OWOD according to open-set metrics. Our model achieves superior performance in terms of traditional evaluation metrics in most cases. Note that these metrics are only computed in the first three tasks because all classes are known in the task 4. The bold represents the best performance on the metric. Overall, our method has significantly improved its performance in unknown class detection, indicating that the objectness-driven discovery module we added has successfully suppressed the unknown discovery uncertainty. At the same time, the performance in known class detection has been more effectively maintained, indicating that our parallel decoupled architecture effectively suppresses the known discrimination uncertainty.
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
### Hybrid Protocols  
The proactive and reactive routing techniques have been combined to create the hybrid routing protocol. It is believed that the protocols at issue combine elements from both of the aforementioned groups. It is envisaged that the utilization of this scenario will allow for a reduction in the overheads associated with route discovery, as well as the realization of the benefits of both proactive and reactive routing methods.
# EduTVA: Metadata Schema for Educational Audiovisual Contents in Digital Television Environments
## 5 Results and Discussion
### Acceptance of Educational Metadata
In addition to the EduTVA acceptance questionnaire, we employed the SUS questionnaire [52] for evaluating the usability of EduTVA Tool and detecting any bias in the acceptance survey due to usage misunderstanding of the tool. The responses from the participants indicate a strong agreement with the positive questions and a strong disagreement with the negative questions, resulting in a SUS score of 86.17 for EduTVATool. Note this score is greater than the average SUS score of 68 that experts defined based on multiple studies [59]. Therefore, EduTVATool qualifies as usable, allowing us to infer that no bias affected the EduTVA acceptance questionnaire due to usability issues of the tool. Figure 5: Responses to question 4 from the EduTVA acceptance questionnaire. Finally, recall that an expert supervised the participants during the whole evaluation process to measure and observe their behavior. Regarding the two objective metrics, first, all the participants completed the given task successfully, achieving a task successful completion of 100%. Second, all the participants completed the task without any errors providing an error-free rate of 100%. On the other hand, the supervising expert observed the following behavior from the participants. 1. Half of the participants filled in the metadata form while watching the audiovisual content, whereas the other half watched the content first and then filled out the metadata form. 2. All the participants reviewed the tooltip attached to every metadata field but only a few opened the tool's general support. 3. A third part of the participants updated the content default description, particularly, the genre taxonomy by selecting the related knowledge areas (e.g., physics, history). 4. All the participants used the video playback options (e.g., pause, play, forward, rewind) to create the content segments. 5. 46,66% of the participants created segments (two to five segments). 6. Some participants read the content default description before starting the mark-up. 7. Some participants watched the audiovisual content on full-screen. 8. The participants displayed the tool Web interface on diverse screen sizes without having any visualization issues. 9. Most participants entered similar values for the educational metadata, particularly, for the fields that provide a list of values to select. Based on these behavior observations, we can conclude that the EduTVA metadata schema enables both simple and detailed descriptions of educational content (items 3 and 5). Moreover, metadata with a list of possible values potentially promotes similar descriptions of educational content with slight differences in the number of segments (items 5 and 9). Finally, EduTVA tool is flexible for marking up audiovisual content (items 1, 4, 7, and 8) and provides help support to users (items 2 and 6).
## 4 Results and Discussion
### Effect of Number of Sublayers  
As was noted above, vertical inhomogeneity in the HSQ-NW composites with \"_pyramidal_\" NWs assumes subdivision of the core layer into sublayers (Figure 5). To estimate the effect of such finer simulation, the effective parameters of \"_pyramidal_\" shape NWs have been determined within the three-layer and four-layer models for one example (Table 3, Figure 10).  
One can see that, though the elastic constants and density are sufficiently different with \(M=3\) and \(M=4\), both models ensure the passage of the dispersion curves through the experimental points. The general form of the curves is also little affected by the transition from \(M=3\) to \(M=4\). The difference in the HSQ-NW effective parameters can be explained by the notable difference in the obtained thicknesses, which were also variable: \(h_{2}=1001\,\mathrm{nm}\) with \(M=3\) and \(h_{2}=314+528=824\,\mathrm{nm}\) with \(M=4\).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline  & \(\mathbf{C_{11}}\) & \(\mathbf{C_{33}}\) & \(\mathbf{C_{13}}\) & \(\mathbf{C_{44}}\) & \(\mathbf{C_{66}}\) & \(\rho\) & \(\mathbf{h}\) & \(\mathbf{E_{x}}\) & \(\mathbf{E_{z}}\) & \(\mathbf{\mu_{z}}\) & \(\
u_{x}\) & \(\
u_{z}\) \\ \hline CrPt & & & & & & & & & & & & & \\ \(M=3\) & 349 & 349 & 210 & 70 & 70 & 20 & 80 & 3.21 & 47.0 & 8.14 & 0.48 & 0.24 \\ \(M=4\) & 344 & 344 & 222 & 61 & 61 & 19 & 144 & & & & & \\ \hline HSQ-NW400 & & & & & & & & & & & & \\ \(M=3\) & 4.21 & 47.7 & 1.50 & 8.14 & 1.09 & 1.52 & 1001 & 3.21 & 47.0 & 8.14 & 0.48 & 0.24 \\ \(M=4\), 2.1 & 50.2 & 80.8 & 6.55 & 2.92 & 17.5 & 2.92 & 314 & 45.4 & 79.5 & 2.92 & 0.30 & 0.10 \\  & 2.2 & 80.5 & 94.9 & 14.2 & 363 & 31.4 & 3.13 & 528 & 75.3 & 90.8 & 363 & 0.20 & 0.15 \\ \hline \end{tabular}
\end{table}
Table 3: Effective parameters of the CrPt coating and HSQ-NW400 composition obtained for the sample shown in Figure 2 within the 3-layer (\(M=3\)) and 4-layer (\(M=4\)) models (“_pyramidal_” NWs, \(t=400\)\({}^{\circ}\)C)  
Figure 9: Experimental points (bold dots), \(|K_{33}|\) level-line image (**a**) and slowness dispersion curves (**b**); “_pyramidal_” sample Figure 2.
###### Abstract  
The GEROS-ISS (GNSS reflectometry, Radio Occultation and Scatterometry onboard International Space Station) is an innovative experiment for climate research, proposed in 2011 within a call of the European Space Agency (ESA). This proposal was the only one selected for further studies by ESA out of \(\sim\)25 ones that were submitted. In this work, the instrument performance for the near-nadir altimetry (GNSS-R) mode is assessed, including the effects of multi-path in the ISS structure, the electromagnetic-bias, and the orbital height decay. In the absence of ionospheric scintillations, the altimetry rms error is \(<\)50 cm for a swath \(<\)-250 km and for U\({}_{10}<\)10 m/s. If the transmitted power is 3 dB higher (likely to happen at beginning of life of the GNSS spacecrafts), mission requirements (rms error is \(<\)50 cm) are met for all ISS heights and for U\({}_{10}\) up to 15 m/s. However, around 1.5 GHz, the ionosphere can induce significant fading, from 2 to \(>\)20 dB at equatorial regions, mainly after sunset, which will seriously degrade the altimetry and the scatterometry performances of the instrument.  
GNSS-R; altimetry; error budget; ionosphere; scintillations +
Footnote †: journal: Sensors
# Improving Response Time for Crowd Management in Hajj
## 4 Experimentation
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline MAC Protocol & CSMA \\ Routing Protocol & RPL Lite \\ Transmission & IEEE 802.15.4 (Channel 26) \\ Band & 2400–2483.5 DSSS \\ Bitrate & Up to 250 kbps \\ Modulation & O-QPSK \\ Number of UAVs & 17 \\ UAV Speeds (m/s) & 1 to 17 (3.6 to 61.2 km/h) \\ Base Station Range Radius, R & 500 m \\ Base Station Coverage on Road & 1000 m \\ Travel Distance per node & 2000 m \\ Image Resolution & 512 \(\times\) 512 \\ Image Type & 8-bit grayscale \\ Total Pixels & 262,144 \\ PRoFFAN Packet Payload Size & 100 bytes \\ Packets per image & 2622 packets \\ Number of Image Layers & 2 \\ Bit Planes (High Priority) & 1–4 (4 MSBs) \\ Bit Planes (Low Priority) & 5–8 (4 LSBs) \\ Packet Rate & 32 packets per second \\ PRoFFAN Queue Length & 32 \\ Number of Simulations & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation Parameters. Figure 5: Experiment Scenario. PSNR and SSIM. Simulations were carried out with PRoFFAN and without PRoFFAN to see the difference. Experiment results are explained in the next section.
## 2 Materials and Methods
### Sequencing, De Novo Assembly, and Bioinformatic Tools  
Genomic DNA from _Desarmillaria_ was isolated using HP Fungal DNA Kit (Omega, Guangzhou, China) according to the manufacturer's instructions. Further purification was conducted using RNase digestion, chloroform extraction, and isopropanol precipitation [29]. The high molecular weight gDNA was washed twice with 75% ethanol, and dissolved in 200 \(\mathrm{\SIUnitSymbolMicro h\SIUnitSymbolMicro h\SIUnitSymbolMicro h\SIUnitSymbolMicro h \SIUnitSymbolMicro h\SIUnitSymbolMicro h\SIUnitSymbolMicro h\SIUnitSymbolMicro h \SIUnitSymbolMicro h\SIUnitSymbolMicro h\SIUnitSymbolMicro h\SIUnitSymbolMicro h \SIUnitSymbolMicro h\SIUnitSymbolMicro h\SIUnitSymbolMicro  
Fisher, Waltham, MA, USA). Shotgun sequencing was performed in the laboratory of at Shanghai Majorbio Bio-pharm Technology Co. Ltd. (Shanghai, China) using Illumina Hiseq 2000 platform. The PE150 (pair-end) libraries with a mean insert size 400 bp were subjected to PacBio sequencing. Adapter sequences were trimmed and short reads were formatted using Trimmomatic v. 0.36 [30] with minimum quality of 19 and minimum length of 30 bp. Quality was assessed using SMRT Analysis software [31].  
The obtained sequence reads were assembled into contigs and scaffolds using SOAPdenovo v. 2.04 [32] and Canu v. 1.7 [33]. K-mer values were automatically selected based on read length and data type. The final scaffold set was verified for miss-assemblies using the BUSCO v3.0 and corrected if necessary [34]. Gene annotations of the coding sequences were formatted to GenBank database for BLASTP alignment or obtained via AUGUSTUS program [35] and verified on the basis of homologous proteins in the NCBI database. The gene ontology (GO) functional annotation was performed using Blast2 go [36]. In parallel, protein annotations were also performed using KEGG (Kyoto Encyclopedia of Genes and Genomes), Pfam [37], and Swiss-prot databases [38].