esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Statistical Inference  
The most popular statistical test for machine learning algorithms is _resampled paired t-test_ and is used to measure the performance of CUIL over the algorithm _Learn++_. The total available samples \((S=20,000)\) is divided into train set \((T_{r})\) and test set \((T_{e})\). The train set is trained using both the algorithms _Learn++_ and _ELM++_ and the test set is used to test. Six sets \((n)\) were conducted for both algorithms and the proportion of misclassified samples \((d)\) were calculated. The mean difference \((\bar{d})\) and standard deviation \((\sigma)\) are calculated using the misclassified samples. Using the equations \(T=\frac{\bar{d}}{\sigma_{error}}\) and \(\sigma_{error}=\frac{\sigma}{\sqrt{n}}\), the value of \(T\) is calculated.  
Let us test the null hypothesis \(H_{o}\) that _CUIL_ is equivalent to _Learn++_ against the alternative hypothesis \(H_{1}\) that _CUIL_ is better that _Learn++_. Using the Table 7, we calculated mean difference \(\bar{d}\) as 2.83, standard deviation \(\sigma\) as 2.815 and \(T\) value as 2.4625. Since the calculated T value is greater than t-tabled value at 5% level of significance (2.015), we reject \(H_{o}\) and accept \(H_{1}\), i.e., _CUIL_ is better than _Learn++_.
## 4 Deep Reinforcement Learning-Based Algorithms
### K-Nearest Neighbors and Reward Penalization  
DDPG was originally designed to handle continuous action spaces. However, resource allocation is a discrete decision problem where the combination of the resource allocation cannot exceed the available total value. Inspired by the approach in [15], we use the KNN to discretize DDPG to adapt it to discrete action spaces. The basic idea is to use a continuous-based algorithm to generate an initial continuous action first. Then, the K nearest discrete actions are found using the KNN algorithm [23]. We chose the nearest neighboring action, as shown in Figure 4.  
In terms of computational overhead, the DDPG algorithm has a better training performance because the action generated at the output of its actor neural network is related to only one neuron, while the other methods have one neuron at the output of their neural network for each possible combination of actions, which in our scenario amounts to approximately 500,000 combinations. Therefore, our proposed approach that uses the KNN algorithm improves the performance of the DDPG-KRP algorithm as well as the efficiency of the DDPG considering large networks. Several recent works have attempted to solve the problem of some RL approaches presenting a large discrete action space by discretizing the continuous action space [15; 25]. In this sense, as shown by the proposed Algorithm 1,a KNN approximation is used because of its agile search in logarithmic time and maps a continuous action space to a discrete action space [15; 23]. We propose applying a method of eliminating undesired actions to the DDPG agent that consists of penalizing the reward function, i.e., during the training phase, if the actor-network generates an action that does not satisfy the SLA constraints and exceeds transmission power available, the reward will be a value of 0.  
This work presents an advance by proposing a joint allocation of bandwidth and power in a complex wireless network scenario composed of network slicing and D2D communication since few works in the literature present resource allocation in mobile networks considering these two next-generation wireless network technologies. In this work, we propose an improvement to the DDPG algorithm using KNN and reward penalization to make its learning more efficient so that resource allocation is more adaptive to this new mobile communication scenario and meets the SLA requirements of each network slice.
## 4 Case Study: NSS Data from a UK University
### Phase 1: Textual Analysis with Machine Learning
#### 4.1.2 Results of Topic Modeling on NSS Data  
This step automatically lists the important topics among the categorized statements. For each category, we applied the LDA topic-modeling method to summarize the top-10 topics. This generated a hundred topics. For brevity, Figure 8 shows the results for the 'Assessment and Feedback' category as an example; the rest are in the Appendix. The first observations were that the topic-modeling method was able to identify thematically coherent topics (with a topic weighting to reflect their relevance) and also represent the underlying category. However, human interpretation was required since each topic was described by a set of seven words, and some topics could be better summarized as a single issue. For example, both Topics 3 and 10 indicated 'Timely feedback to assessment'; Topics 2, 3, and 8 suggested 'Cleaner assessment criteria'; and Topics 5, 7, 8, and 9 related to 'Assessment deadlines'.  
Figure 7: Relative frequency (%) of statements (manually annotated and auto-classified) per category using the annotation scheme developed.  
Using human interpretation and a holistic view of a hundred auto-summarized topics, we were able to summarize the top-10 issues (from more than 10 topics, as demonstrated above) across all categories. Although this process was not made fully automatic, we consider that human involvement was far less than that required to process all 10,328 statements manually. Finally, top-10 issues identified are as labeled (II-I10), which are further discussed to inform the teaching interventions in the next stage.
# FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks
## 5 Performance Evaluation
### Attack-Based Network
#### 5.1.5 Random Placement of Increasing Attackers  
In this experiment, multiple senders (sources) and receivers (destinations) were tested in the presence of increasing multiple attackers within the network. The set up comprised of 6 random nodes from the left side of the terrain, representing the locations of events of interest. The nodes were tasked with sending 6 CBR flows to 2 random nodes on the right-hand side of the terrain representing the destinations (sink nodes), such that each node receives 3 CBR flows from the 6 sending nodes. Results shown in the graph (Figure 8) are the average of 100 simulation runs for each increasing number of attackers.  
Figure 8 shows successful packet delivery plotted against an increasing number of attackers randomly deployed in the network. DWSIGF-P employs greedy forwarding strategy that could easily bypass attackers as long as they do not fall on the direct line centered at the sextant towards the destination. This directed forwarding enabled DWSIGF-P to show better performance than DWSIGF-R. DWSIGF-R on the other hand uses random forwarding; this random propagation influence the frequent selection of randomly placed attackers, causing substantial packet losses during the routing process. The unrivaled performance in FuGeF is attributed to its spontaneous and yet directed propagation pattern. The pattern was as a result of FLS consensus after processing nodes parameters (Progressive distance, connectivity cost, and remaining energy) as they change during the routing process. FuGeF  
Figure 8: Packet delivery.  
forwarding strategy emerged to be more reliable than both DWSIGF-P and DWSIGF-R (they only utilize the predictive IDTD parameter for forwarding).
# Carrier-to-Noise-Threshold Filtering on Off-Shore Wind Lidar Measurements
## 2 Site and Instrumentation  
The observations for this study have been collected off-shore in the North Sea at the German Research Platform FINO3 (Forschungsglattform in Nord- und Ostsee Nr. 3) located at (55.19501\({}^{\circ}\) N; 7.15836\({}^{\circ}\) E) 80 km west of the Danish island Reme, Figure 1, at a water depth of 22 m. The approved offshore wind farms Sandbank 24, Nordlicher Grund, DanTysk, and OSB Butendiek lie in the near vicinity, but the construction of these was not yet initiated during the measuring campaign.  
The measurements were carried out with a \(\sim\) 600 m range wind Doppler lidar in combination with a 100 m tall meteorological mast equipped with cup-anemometers at 50 m, 70 m, and 90 m height in combination with wind van  
Figure 1: Map showing the position of the FINO3 (Forschungsglattform in Nord- und Ostsee Nr. 3) research platform where the measurements were carried out. Water is marked in white and land in grey.
## 3 Path Planning
### Potential Field Algorithm Simulation
In order to compare the computation time of the potential field and the modified potential field when the number of point clouds is one, three, or 100, the average computation time and standard deviation were obtained by performing 1000 calculations under the conditions shown in Table 2; the results are shown in Figure 9.  
Since the LiDAR mounted on the UGV updates the new point cloud at a frequency of 10 Hz, the path generation frequency was also set to 10 Hz. The existing method took about 10% of the time to create a path at each step, whereas the modified method showed an improvement to 2% of the time. Figure 8: Potential field simulation: (**a**) simulation result of potential field algorithm with two obstacles; (**b**) simulation result of modified potential field algorithm with two obstacles. Figure 7: Potential field simulation: (**a**) simulation result of potential field algorithm with single obstacle; (**b**) simulation result of modified potential field algorithm with single obstacle.
## 2 Datasets and Methods
### Datasets
#### 2.2.2 Climate Data  
To analyze the climate conditions for forming CUE extremes, we adopted the monthly temperature, precipitation, and solar radiation from the CRU-NCEP dataset [26]. In this study, all data were resampled into the spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) by the nearest resample method.
## 2 Methods
### Implementation Process  
#### 2.2.1 Data Preprocessing  
In computer image recognition applications, foreground elements are typically encoded as 1, while background features are represented as 0, with analysis focused on foreground data. However, conventional single-directional DEM processing can only effectively delineate ridge features. To obtain valley zones, DEM inversion is required to create complementary elevation profiles. This necessitates the integrated use of both forward and inverse DEM datasets for comprehensive terrain analysis.  
To address the inherent noise sensitivity and directional artifacts arising from direct curvature computation on raw DEM data, we propose a two-stage multiscale filtering strategy that effectively balances noise suppression and topographic feature preservation. The initial preprocessing phase employs a low-pass filter to eliminate high-frequency elevation perturbations, which typically manifest as localized spikes and random variations. Subsequently, an anisotropic diffusion algorithm is applied to selectively attenuate directional streaks associated with data acquisition artifacts and geometric distortions while maintaining critical morphological elements such as ridge crests and valley bottoms. This hierarchical filtering approach ensures that both microscale noise components and macroscale linear features are appropriately conditioned, thereby enhancing the overall signal-to-noise ratio for subsequent curvature-based segmentation.  
Based on [PERSON]'s theorem of differential geometry, mean curvature (\(K\)) represents the averages of the profile curvature and the tangent curvature:  
\[K=\frac{k_{v}+k_{h}}{2} \tag{1}\]  
where \(k_{v}\) denotes the profile curvature (first derivative of the slope along flowlines) and \(k_{h}\) denotes represents the tangent curvature (second derivative of the aspect angle relative to flowlines). This combined curvature metric effectively captures both convex (ridge) and concave (valley) geometric elements. The profile curvature quantifies spatial slope variations, while the tangent curvature measures directional changes in the aspect angle, providing complementary geometric information for terrain characterization.  
Following the two-stage filtering process, the conditioned DEM undergoes automated binary segmentation based on mean curvature values to achieve topological feature extraction. By assigning convex regions (K > 0) a numerical value of 1 and concave/flat regions (K \(\leq\) 0) a numerical value of 0, this methodology effectively transforms continuous elevation data into a discrete representation of terrain elements. The thresholding operation not only distinguishes ridge-valley networks through curvature-based classification but also preserves geometric coherence by maintaining original surface connectivity and drainage patterns. This critical step establishes a clear separation between positive and negative curvature features while ensuring compatibility with downstream analyses that require binary terrain representations.
## 3 Data Preparation
### Data Collection Method
#### 3.3.2 Data on Fixed Objects and Road Geometry  
The TAAS database provides information on the point of collision, time, severity, violation of associated regulations, and weather, but does not provide information on the road cross-sectional characteristics, curve type of road, and AADT. In particular, because the cross-sectional characteristics of the road (type of fixed objects, distance between the road-way and the fixed object, and fixed object density) primarily contribute to crashes [11], the research team made extensive efforts to identify the characteristics that were not provided in the TAAS database. We used the KakaoMap Street View application to identify roadside elements, and this application provides images and street views of the surrounding environment of most roads in Korea from 2009 to 2021. For the road alignment characteristics, the type of horizontal alignment, radius of the curve, vertical slope, and the number of entrances/exits were identified for the four to six lane highways where the RORC events occurred using the digital topographic map (scale = 1/1000) of the Land Information Platform (LIP), which is a database managed by the National Geographic Information Institute (NGII).
## 5 Case Study: Practical Implementation of DT for HEMS
### Digital Twin for HEMS  
This section presents the implementation and functionality of the DT framework for an HEMS, focusing on its real-world application. Initially, it discusses the integration of IoT sensors within the DT environment, enabling real-time operational monitoring and fault detection. Furthermore, the system's capability to track and visualize indoor environmental parameters is explored, such as temperature and humidity, providing users with actionable insights. Finally, it highlights the use of thermal comfort metrics, including the PMV index, to optimize indoor conditions while ensuring energy efficiency. These components collectively validate the DT's role in enhancing energy management, user engagement, and system reliability.
## 5 Experiments
### Dataset Description  
The buoy dataset has many meteorological measurements such as wind direction, wind speed, gust speed, wave height, air temperature, water temperature, tide, etc. However, buoy measurements from the year 2005 have many missing values or missing features. We have selected buoys which have sufficient historical data from the 2005 hurricane season for comparison purposes. For each buoy data reading, there are two contextual attributes: a time stamp (temporal context) attribute and latitude-longitude (spatial context) attribute which is static for each buoy. Beside these contextual attributes there are six behavioral attributes which are mostly available for each buoy used for this experiment: wind direction, wind speed, gust speed, barometric pressure, air temperature, and water temperature. After having eliminated buoys with many missing features and/or values and the ones that were damaged during the hurricane season we have selected 21 buoys and their data were included in our experiment. Those buoys are: 41001, 42001, 42003, 42036, 42038, 42039, 42040, FWYF1, BURL1, BYGL1, CDRF1, FWYF1, GDIL1, MLRF1, SANF1, SSOF1, SMKF1, SPGF1, VCAF1, KYWF1, NPSF1, and LONF1. Their locations are depicted in Figure 5. Python programming language and Geopandas ([[http://geopandas.org/](http://geopandas.org/)]([http://geopandas.org/](http://geopandas.org/))), which is an open source project to work with geospatial data in Python, are used to depict buoy locations on map.  
Figure 5: The location of buoys used in the experiment.
## 4. Discussion  
This review provided an overview of the potential of TDDSs, in particular for patients with AD and PD. Neurodegenerative diseases represent a major challenge for the future, because of the worldwide increase in prevalence and incidence, and the ever-higher socio-economic costs [75]. While waiting for disease-modifying treatments, the symptomatic control of motor and cognitive disturbances is the main therapeutic goal for these conditions [76]. However, because of the disease progression and growing disability, drug administration by the conventional oral route is not so easy and effective at later disease stages. For a long time in the oral cavity, drugs have been applied to solve diseases of the mouth itself. In this field, great improvements have been made in the long-term administration. Accordingly, TDDSs emerge as an excellent alternative to conventional oral administration [77; 78]. Available data, indeed, show that TDDSs offer good clinical benefits in PD and AD patients, without affecting the cytoarchitecture of the mouth. Even more satisfying and encouraging are the results of the various in vivo and in vitro clinical trials of the innovative IntelliDrug and Orafuse systems. Therefore, in the future, they may become valid tools to deliver symptomatic therapy in neurodegenerative patients at later stages. The systems might improve patient compliance, which is particularly low in these individuals, also ensuring constant drug flow [79]. Likewise, the devices can help to keepstable or even lower drug dosage, limiting the risk of side effects. Besides neurodegenerative disease, there is also evidence that TDDSs might be used for the treatment of local oral diseases, such as periodontal pockets and mucositis [80]. However, further studies are needed to confirm these encouraging but preliminary findings.
# New Results for the Error Rate Performance of LoRa Systems over Fading Channels
## 3 Main Results
### Symbol Error Probability for Generalized Fading Channels Using a Mixture Gamma Distribution  
In what follows, we present analytical results for the SER of LoRa systems assuming generalized fading channels for which the PDF of the SNR can be expressed as a mixture gamma distribution. As was shown in [40], the proposed approach is valid for a plethora of fading distributions, including the \(\kappa\)-\(\mu\), the \(\eta\)-\(\mu\) and composite fading/shadowing channels such as the generalized-K and the Suzuki ones. The PDF of \(h^{2}\) can be expressed as [40] (Equation (1))  
\[f_{h^{2}}(y)=\sum_{i=1}^{\mathrm{N_{\mathrm{terms}}}}a_{i}y^{\beta_{i}-1}e^{- \zeta_{i}y}, \tag{43}\]  
where \(\mathrm{N_{\mathrm{terms}}}\) is the number of terms required for a given accuracy, and \(a_{i}\), \(\beta_{i}\) and \(\zeta_{i}\) are the parameters of the \(i\)th Gamma component. The parameter \(\mathrm{N_{\mathrm{terms}}}\) can be selected so that the first \(k\) moments of the original and the approximate distributions are matched or the Kullback-Leibler distance of the original and the approximate distributions is minimized [40]. For such channels, an exact expression for \(P_{s}\) can be obtained using the following proposition.  
**Proposition 9**.: _The exact symbol error probability of LoRa systems operating under generalized fading channels can be expressed in terms of a single integral as_  
\[P_{s}^{\mathrm{Sen}} =\frac{1}{2}\sum_{i=1}^{\mathrm{N_{\mathrm{terms}}}}a_{i}(N\gamma +\zeta_{i})^{-\beta_{i}}\Gamma(\beta_{i})\int_{0}^{\infty}e^{-x/2}\] \[\times[1-(1-e^{-x/2})^{L}]\mathrm{L}_{-\beta_{i}}\bigg{[}\frac{N \gamma x}{2(N\gamma+\zeta_{i})}\bigg{]}\mathrm{d}x \tag{44}\]  
Proof.: The proof can be readily deduced by following the same steps as in the proof of Proposition 1.
# Smartphone-Based NFC Potentiostat for Wireless Electrochemical Sensing
## 3 Results
### Application of NFC potentiostat  
To evaluate the application of the NFC potentiostat, it was used to measure the ascorbic acid concentration and determine ascorbic acid in spiked commercial drinking water. The detection limit of standard ascorbic acid was determined by cyclic voltammetry over the potential range from 200 to 800 mV. Cyclic voltammograms of different concentrations of ascorbic acid (0.1-1 mg/mL) are shown in Figure 7a. The current at 550 mV corresponds to the oxidation of ascorbic acid [19,52]. Therefore, the current at 550 mV was used to plot the calibration curve as shown in Figure 7b. The linear regression equation of ascorbic acid measurement was y(uA) = 4.709x(mg/mL) + 1.198, with a correlation coefficient of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Molarity** & \multicolumn{2}{c}{**Commercial Potentiostat**} & \multicolumn{2}{c}{**NFC Potentiostat**} \\ \hline  & **AVG (\(\mu\)A) \(\pm\) SD** & \%**RSD** & **AVG (\(\mu\)A) \(\pm\) SD** & \%**RSD** \\ \(25\times 10^{-6}\) & \(-\)0.037 \(\pm\) 0.0007 & \(-\)2.55 & \(-\)0.042 \(\pm\) 0.0003 & \(-\)0.91 \\ \(1000\times 10^{-6}\) & \(-\)2.064 \(\pm\) 0.0572 & \(-\)3.40 & \(-\)2.071 \(\pm\) 0.1309 & \(-\)7.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average (AVG), standard deviation (SD) and relative standard deviation (RSD) value of the current obtained from both potentiostats in chronoamperometric measurements.  
Figure 5: The chronoamperometric measurement of 25 \(\upmu\)M ferricyanide measured with (a) NFC potentiostat (red) and (b) commercial potentiostat (black).  
Figure 6: (a) The chronoamperometry recorded in 25–100 \(\upmu\)M ferricyanide solution measured by the NFC potentiostat (red) and the commercial potentiostat (black). (b) The linear correlation between current and concentration of ferricyanide.  
0.998. The limit of detection (LOD) was calculated from 3 s/m and the limit of quantitation (LOQ) was obtained from 10 s/m where s is the standard deviation of blank (\(n=10\)) and s is the slope of the calibration curve. According to the equation, the limit of detection and the limit of quantitation were calculated to be 0.0024 mg/mL and 0.0080 mg/mL, respectively. [PERSON] et al. reported that the detection limit for measuring ascorbic acid using the pencil electrode was 0.0326 mg/mL [52]. So, the limit of detection for ascorbic acid determination using a screen-printed carbon electrode in this study was lower than the previous work. The proposed NFC potentiostat was employed to determine ascorbic acid in spiked commercial drinking water. The results are shown in Table 3. The recoveries were 98.78% \(\pm\) 4.74, 95.80% \(\pm\) 2.87 and 92.69% \(\pm\) 2.35 for spiking ascorbic acid at concentrations of 0.2, 0.4 and 0.6 mg/mL, respectively. The results indicated that the NFC potentiostat has acceptable accuracy for ascorbic acid measurement.