esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Rheological Behavior
From the data of the complex viscosity at different temperatures and frequencies, an activation energy (E\({}_{\text{act}}\)) for the viscous flow of the investigated samples can be evaluated by using an Arrhenius type equation (Equation (6)), as previously reported [26; 27]. \[\log(\ \eta\ \prime)=\log(\eta_{\text{o}})-\frac{\text{E}_{\text{act}}}{2.303\ \text{R}\ \text{T}} \tag{8}\]  
where \(\eta_{0}\) is a pre-exponential factor, T is the absolute temperature for the rheological test and R is the universal gas constant of 8.314 J/molK. The value of intercept \(\eta_{0}\) formally represents complex viscosity at infinite temperature. Table 1 reports the values of E\({}_{\text{act}}\) for neat ABS and its nanocomposites at different frequencies. The average activation energy for the viscous flow of neat ABS was found to be 107 kJ/mol. With the addition of nanofillers, the decrease of the activation energy suggests that both the investigated carbonaceous nanofillers favor the flow of polymer chains in a pseudo-Newtonian zone. In the presence of graphene, due to the possible lubricating effect of this lamellar filler, a minimum of the activation energy can be observed. Moreover, lower wettability with the polymer matrix and more particle-particle interactions may occur [25]. It is also interesting to note that the activation energy evaluated in the melt flow process follows an opposite trend with 87, 94, and 117 kJ/mol, for ABS, ABS/GNP and ABS/CNT, respectively as shown in our previous paper [21]. In this latter case, the difference could be attributed to the higher shear rate with respect to the rheological plate-plate experiments, and to the consequent pseudoplastic behavior of polymer melt during extrusion where both GNP and CNT played the direct role of effectively reduction the viscosity in the molten flow. It is also interesting to underline that the data in Table 1 show the tendency of ABS to decrease the activation energy at higher frequencies, in contrast to ABS/GNP and ABS/CNT. Figure 6: Cole-Cole plots of imaginary viscosity (\(\eta\)”) versus real viscosity (\(\eta\)’) of ABS, ABS/GNP and ABS/CNT nanocomposites at 250 and 280 \({}^{\circ}\)C.
## 2 Related Work
### Regional Language Variations in NLP  
Regional variations in language, encompassing dialects, accents, and colloquialisms, pose significant challenges for NLP tasks, including emotion detection. Models trained on data from one region may not generalize well to other regions due to differences in linguistic patterns and cultural norms. This has led to a growing body of research focused on capturing and modeling regional language variations in NLP.  
One approach is to incorporate region-specific information into the models. For example, [PERSON] and [PERSON] (2017) [PERSON] and [PERSON] (2017) explored the use of geolocation data to enhance sentiment analysis, demonstrating that incorporating regional context can improve model performance. Another approach is to develop region-specific language models. This involves training separate models on data from different regions, allowing each model to specialize in the linguistic characteristics of its respective region [PERSON] and [PERSON] (2017).  
The study of regional language variations also has implications for data collection and annotation. To ensure that NLP models are fair and inclusive, it is crucial to collect and annotate data that are representative of all regions and linguistic communities. This requires careful consideration of sampling strategies and annotation guidelines to avoid biases and ensure that all voices are represented.  
Despite the growing use of synthetic datasets in NLP, many studies do not explicitly consider regional linguistic variations, which can impact model generalization and performance. The existing research on synthetic data generation has primarily focused on general-purpose datasets, often assuming linguistic uniformity across English-speaking populations [19; 20]. However, regional language variations influence how emotions are expressed, which has been observed in sentiment analysis and other NLP tasks [17; 21]. Failing to capture these variations may lead to biases in emotion detection models, limiting their applicability across diverse populations. This study builds upon the existing literature by investigating the use of region-specific synthetic datasets for emotion detection. By leveraging the capabilities of LLMs to generate data that capture regional language variations, we aim to develop more robust and culturally sensitive emotion detection models that can be deployed in diverse real-world settings.
## 3 Background
### Cross-Technology Interference Sources
#### 3.1.1 IEEE 802.11  
The prevalent WLAN networks in the 2.4-GHz band are based on the IEEE 802.11 b/g/n specifications. The IEEE 802.11 b/g PHY supports up to 14 channels, 20 MHz wide each. On the other hand, the IEEE 802.11 n can support both the 20 MHz- or 40 MHz-wide channels. There are only three non-overlapping usable channels in the U.S. and other countries with similar regulations (Channels 1, 6, 11, with 25-MHz separation) and four in Europe (Channels 1, 5, 9, 13, with 20-MHz separation). The transmit power of WLAN devices ranges from 15 dBm-20 dBm, and depending on the underlying standard, different modulation schemes and data-rates are available. However, the maximum air-time of an IEEE 802.11 packet remains below 600 us. The standard specifies a carrier-sense multiple access with collision-avoidance (CSMA/CA)-based MAC with certain timing rules between the consecutive packets. In commonly-used infrastructure mode, an access point advertises the network by sending the periodic beacon frames. For compatibility reasons and in order to increase the network detection range, the beacons are usually sent at the lowest data rate (1 or 2 Mb/s). The default beacon frequency period is 100 time units, which is equal to 102.4 ms [26]. The above-stated heterogeneous medium access rules and PHY specifications for WLAN networks render ZigBee systems vulnerable. Firstly, WLAN networks deployed on non-overlapping channel allocation, such as the typical \(\{1,6,11\}\) configuration, leave a small number of IFCs for ZigBee. Secondly, the high-power concurrent transmission on an overlapping channel from a WLAN device will likely cause severe packet corruption in an IEEE 802.15.4 packet. Thirdly, the duration of an IEEE 802.15.4 clear-channel assessment (CCA) is 128 us, while 192 us [14] are required to switch from CCA to transmit mode. Conversely, the IEEE 802.11 CCA procedure takes 28 us, while the switching time is negligible. As a result, the chances of the corruption of a ZigBee packet are very high, as the WLAN transmission can disrupt the ZigBee transmission during the switching mode.
## 4 GTP-Dependent Activation of CTPSs from Other Species
### Plants  
The kinetic properties of CTPSs from plants have not been extensively characterized. Interestingly, the enzyme from _Arabidopsis thaliana_ (_At_CTPS) occurs as five isoforms and six putative isoforms that have been identified in rice (endospermless variant from _Oryza sativa_ var. _japonic_ cultivar Hwayoung) [90]. _At_CTPS1-4 are expressed throughout the plant tissues, while _At_CTPS5 is expressed only in developing embryos [91]. This enzyme also undergoes filament formation as previously demonstrated for the yeast, fruit fly, human, and _E. coli_ enzymes (vide infra). GTP was shown to activate _At_CTPS3 with an increase in the concentration of GTP from 0.05 mM to a saturating concentration of 1.0 mM GTP, increasing the rate of Gln-dependent CTP formation nearly 5-fold with an apparent \(K_{\rm A}\) value of ~0.25 mM.
# Design and Depth Control of a Buoyancy-Driven Profiling Float
## 4 Results and Analysis  
In order to verify the feasibility of the control method mentioned above, a dynamic model of the float and a linear actuator system model were constructed by Simulink, considering the change of seawater density and the deformation of the electronic chamber. The traditional PD method for depth error, the segment PD method proposed in this paper, and SMC were used to compare the depth control of the float at 20 m, 50 m, and 80 m, respectively. Subsequently, field tests were carried out at three locations in Qiandao Lake, Zhejiang Province, China. Continuous depth control and temperature data ranging from 5 m to 60 m were obtained using the segment PD control proposed in this paper. Finally, the application of the float in thermocline measurement was demonstrated.
## 3 Dynamic Load Generation  
The pavement roughness data are a set of two-dimensional information representing the deviation of the pavement surface elevation relative to the reference plane, which is one of the main excitations causing vehicle vibration. Critical roughness can cause extreme responses which are related to the vehicle speed and the suspension system of the vehicle. One way of considering roughness effect on the pavement stress from the moving vehicle load is to reflect the roughness directly by the surface profile of the pavement model in the finite element analysis (FEA); however, this imposes extremely high requirements on the model meshing and significantly increases the convergence difficulty of the FEA. [PERSON] et al. [17] used a sinusoidal form to simulate the asphalt pavement surface unevenness by finite element method, based on which the dynamic loads were generated through a quarter-car model. This greatly simplified the calculation of pavement surface unevenness; however, the dynamic effects were not investigated by including the standard road roughness level as the excitation source, and there might be difficulties in comparing the results with other dynamic response cases. The other dynamic load generation method is based on the roughness index, as discussed below, which has been used in a few studies [11,13,18,19].
## 5 Questionnaire and Quantification Method Approach
### Accessibility Debt  
Accessibility Debt, as introduced in this study, refers to the barriers participants face due to a lack of immediate usability and inclusive design in competition platforms. These barriers include unintuitive interfaces, fragmented onboarding processes, insufficient documentation, lack of multilingual support, and limited responsiveness to novice needs [74; 154; 155]. Such issues can silently discourage engagement, disproportionately affecting underrepresented participants, and ultimately compromising competition fairness and diversity [9; 24].  
Unlike traditional usability or infrastructure concerns--typically addressed within broader software quality evaluations--Accessibility Debt is conceptualized here as a socio-technical liability capturing both experiential and structural design limitations specific to AI/ML competition platforms. While it overlaps with categories such as Infrastructure Debt (e.g., platform readiness or technical constraints) [50; 137], Documentation Debt (e.g., missing or ambiguous instructions) [134; 135], and Process Debt (e.g., opaque submission workflows or limited user guidance) [38; 72], its focus is distinct: it addresses how barriers in accessibility accumulate into measurable forms of technical debt that hinder participation, particularly for newcomers or non-technical users.  
The decision to introduce Accessibility Debt as a standalone category arose from recurring themes identified in the literature that were not sufficiently represented in existing typologies. Several studies allude to accessibility concerns--such as the absence of onboarding support, multilingual limitations, or high entry barriers--but do not explicitly categorize them under a cohesive technical debt label [8; 18]. Our thematic synthesis consolidated these concerns into a unified and operationalizable category that emphasizes the importance of inclusive and usable design in competition infrastructures [9; 103].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Question** & **Score** & **Answer** & **Calculated Score** \\ \hline Are you detecting direct or hidden feedback loops in & 4 & NO & 4 \\ your model? & & & \\ Is model quality validated before serving? & 5 & YES & \(-5\) \\ Does the model allow debugging by observing step-by-step & 3 & I Don’t Know/ & 3 \\ inference on a single example? & & I Don’t Answer & 2 \\ Overall Rating & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Example—participant perspective: model debt.  
To enable empirical assessment, a dedicated subset of questionnaire items was developed to measure the presence and severity of Accessibility Debt. These items are scored from the organizer's perspective, as the relevant design decisions fall under their control. Nevertheless, we acknowledge that participant feedback could offer valuable complementary insights and may be incorporated in future iterations of the assessment framework.
## 3 The Proposed Protocol  
In this section, we describe the proposed protocol with the following steps--a system model, system initialization, and the proposed authentication methods. Figure 1 summarizes the proposed protocol architecture that introduces three new authentication methods. Table 2 summarizes the system notations and the commonly mentioned variables.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Descriptions** \\ \hline BSs & Base Stations \\ \hline CA & Certificate Authority \\ \hline ECC & Elliptic Curve Cryptography \\ \hline \(Lpid_{i}\) & a list of pseudo identities \((pid_{i1},\;pid_{i2},\;pid_{i3}\ldots,pid_{in}\;)\) \\ \hline \(Lsk_{i}\) & list of secret keys \((sk_{i1},\;sk_{i2},\;sk_{i3},\ldots,sk_{in}\;)\) \\ \hline \(Lpk_{i}\) & a list of the corresponding public keys \((pk_{i1},pk_{i2},pk_{i3},\ldots,pk_{in})\) \\ \hline \(L_{ZID}\) & Zone of ID list \\ \hline \(sk_{iZID}\) & A random integer number represents a secret key of vehicle \(v_{i}\) in each zone \\ \hline \(F_{p}\) & Finite field of elements in the range \(\{1\;and\;p-1\}\). \\ \hline \(G_{1},G_{2}\) & two cyclic additive groups of prime order \(p\) based on the elliptic curve \(E\) over the finite field \(F_{p}\) where \(G_{1}\times G_{2}\to G_{T}\) \\ \hline \(G_{T}\) & Acyclic multiplicative group containing the bilinear pairing result of the two groups \(G_{1},G_{2}\). \\ \hline \(e\) & The bilinear pairing function that maps elements from group \(G_{1}\) and group \(G_{2}\) to group \(G_{T}\) \\ \hline \(g_{2 ZID}\) & Represents the generator point of the group \(G_{2}\) for each zone \\ \hline \hline \end{tabular}
\end{table}
Table 2: The system notations and abbreviations.  
Figure 1: The proposed protocol architecture.
## 1 Introduction
Beamforming techniques are applied in a modified two-stage methodology based on [6; 7], which is adapted to the NULA and the terrestrial environments: the first step is focused on improving the target detection in the CAF domain, and the second one consists of DoA estimation using a spectrum-based algorithm. A real bistatic scenario, with the receiver at the rooftop terrace of the Polytechnic School (University of Alcala), was considered. This scenario was analyzed in [8], and the study is completed with a more accurate estimation of system coverage. In [8], only free space losses were considered. Taking into consideration that the desired targets are on the ground (cars), these estimations were really rough. In this paper, the WinProp (AWE Communications) software is used to obtain more accurate coverage estimations. Results confirm that the designed NULA based on COTS single radiating elements,  
Figure 1: Basic operating scheme of a Passive Radar System (PRS). in combination with the two-stage beamformer, is capable of improving the 2D localization accuracy of terrestrial targets (cars) in semiurban scenarios, providing a low-cost solution for traffic monitoring. The paper is structured as follows: In Section 2, a general description of the passive radar operating principle and the DVB-T signal as IoO is presented. In Section 3, the problem of antenna design for passive radars is formulated, and an NULA is designed with an optimization process, solved using a genetic algorithm. In Section 4, spatial filtering based on weighting vectors for NULA is described. In Section 5, the modified two-steps spatial filtering technique is detailed. In Section 6, results are presented to validate the study with real data acquired in a semiurban scenario. Finally, in Section 7, the main conclusions are summarized.
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 6 Conclusions  
This study aimed to reveal the potentials and limitations of commercial LTE networks for providing cellular connectivity for BVLoS drone operations. In this regard, first, an LTE-powered drone prototype was constructed. The drone is capable of extracting the potential of the current LTE network and provide a packet data rate of 1303 kbps from the drone to the ground station and a packet data rate of 20 kbps from the ground station to the drone with a delay of 94 ms for both uplink and downlink. The results show that for more advanced drone use cases where low latency and high uplink data rate are required, the current 4G network needs to be improved. Furthermore, a comprehensive field trial measurement was conducted to reveal the current cellular network's capability and challenges. The results showed that although the current 4G network is capable of providing command and control, as well as a payload communications link for the drone BVLoS use cases, further enhancements are needed to provide a secure and reliable communications link for advanced use cases of drones, especially in terms of overall network latency and uplink data rate.  
Conceptualization, R.N. and M.S.Z.; methodology, M.A.Z.; software, M.A.Z.; validation, M.A.Z., M.B. and R.N.; formal analysis, M.A.Z., M.B.; investigation, M.A.Z., M.B. and R.N.; resources, M.S.Z.; data curation, M.A.Z.; writing--original draft preparation, M.A.Z.; writing--review and editing, M.A.Z., M.B., R.N. and M.S.Z.; visualization, M.A.Z.; supervision, R.N.; project administration, M.S.Z.; funding acquisition, M.S.Z. All authors have read and agreed to the published version of the manuscript.  
We acknowledge the financial support from Universiti Kebangsaan Malaysia, under the grant DCP-2018-004/3, and Collaborative Research in Engineering, Science and Technology (CREST), under the grant T23C2-19.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data will be made publicly available once the drive test measurement campaign has been completed based on various drone deployment scenarios.  
The authors would like to thank the other research members from project DCP-2018-004/3 that are directly contribute their ideas and suggestion towards the design and measurement activities in this manuscript.  
The authors declare no conflict of interest.
## 4 Experimental Setup and Results
### Ablation Studies of GRM  
To verify the effectiveness of the proposed GRM method, we first quantitatively compare the network with and without the GRM. Table 2 shows the accuracy of multiple networks on the three tasks. For the vanilla network, it can be found that the Inception-V3 achieves the highest accuracy among all the tasks. For example, the accuracy of Inception-V3 on retinal diseases is 94.9, far beyond the 89.9 of the ViT. The inferiority of ViT can be attributed to the difficulty in hyper-parameter tuning and the limited size of the medical image dataset, which significantly increase the risk of over-fitting. By comparing the vanilla network with the one with GRM applied, it can be observed that the accuracy of GRM is unanimously improved among all the tasks, regardless of the network architecture. The highest improvement is from 90.0 to 94.2 in the ResNet-50 on the pneumonia task. A varying degree of improvement is also obtained for the ViT in the three datasets. These encouraging results illustrate that the proposed GRM is broadly applicable to datasets composed of varied modalities and class numbers.  
One of the reasons for the effectiveness of GRM is the adaptive regularization, which helps the network better extract the context information and alleviate the over-fitting issue. To delve into the training procedure, Figure 4 summarizes the training and validation loss of ResNet-50 in the three tasks. It can be observed the regularization effectiveness brought by GRM in the task of retinal diseases in Figure 4a, where the training loss of GRM (red dotted line) decreases slower than the one in the vanilla network (red solid line), implying the GRM helps to mitigate over-fitting to the training dataset. On the contrary, the validation loss of GRM (green dotted line) is distinctly lower than the one of baseline (green solid line), demonstrating that the GRM increases the network's generalization capacity. Similar convergence results can be found in the pneumonia task. The effectiveness of GRM can also be notably found in the glaucoma task, where the validation loss of the vanilla ResNet-50 increases rapidly from the 50 th epoch, and its ascending speed goes faster along the training epochs. This convergence behavior can be commonly observed in the training of networks. By adding GRM to the network, the stability of validation loss is significantly improved, as shown in the green dotted line in Figure 4c. These convergence results confirmed the regularization impact brought by the GRM, which helps combat the over-fitting issue on the training dataset and boosts the generalization ability on the validation dataset.
# Stepped Spillways and Energy Dissipation: A Non-Uniform Step Length Approach
## 4 Conclusions  
Utilizing the optimal design of stepped spillways is essential for maximizing the energy dissipation and, consequently, reducing the construction costs of hydraulic structures. In this article, different stepped spillway configurations with uniform, non-uniform, and semi-uniform step lengths were studied numerically. The results indicated that the air entrainment increases as the step length increases. For concave and convex stepped spillways, it was observed that the amount of air concentration in the concave spillway is more than in the convex one. As a result, it can be considered that the concave stepped spillway is more effective than the convex one in terms of reducing the risk of cavitation. Moreover, it was observed that, among all models, the main factor that influences the energy dissipation is the vortexes formed by the recirculation flow. For the different uniform configurations investigated in this article, the uniform stepped spillway design with a slope of 1V:2.2H was found to be optimum in terms of the energy dissipation performance. However, over all the models, the semi-uniform chute performed better. The results indicated that the vortex generation and dissipation process associated with each vortex became confined between two adjacent cavities on the entire chute when the ratio between the successive step lengths was 1:3, such that the energy dissipation was improved by up to 20%.  
Conceptualization, A.A., and A.R.; methodology, A.A., and A.R.; validation, A.A., formal analysis, A.A., and A.R.; investigation, A.A., and A.R.; resources, A.A.; data curation, A.A., and A.R., writing--original draft preparation, A.A.; writing--review and editing, A.R.; visualization, A.A., and A.R.; supervision, A.R.  
This research received no external funding.  
The authors would like to thank. [PERSON] for his valuable comments and suggestions on an earlier draft of the paper.  
The authors declare no conflict of interest.  
Figure 15: Velocity vectors on semi-uniform stepped spillway configurations over the entire chute.