esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Influence of the Proposed Model's Parameters on Balancing Performance and Clustering Efficiency  
#### 4.3.1 Proximity Thresholds  
DENC and DBSCAN exhibit similar patterns in how their proximity thresholds influence the model's performance. Smaller thresholds result in tighter clusters, enabling the capture of fine-grained correspondences in densely packed ILI sets. However, overly small thresholds in either approach may lead to actual matches being classified as outliers, reducing recall and lowering overall performance. Conversely, larger thresholds allow for clustering features with greater spatial variability. This relaxes the clustering criteria, improving recall by including features within broader spatial boundaries. However, excessive threshold values risk reducing cluster diversity and often result in most clusters being categorized as point matching problems (Category 4).
## 5 Conclusions and Future Work  
In this paper, we compare distributed clustering techniques for wireless sensor networks on the basis of their reconfigurability, and in particular, on the basis of their ability to adapt to changes and disturbances during Mobile Node tracking. The clustering techniques are intended to span a range of pre-defined clusters based on efficient geographic partitioning of sensor nodes around cluster heads (in this case, in the form of Voronoi cells) and _ad hoc_ clusters that are formed \"on the fly\" during the tracking task.  
All three of the clustering techniques showed that they are capable of reconfiguring their structures to adapt to changing shop floor and Mobile Node tracking conditions. However, as would be expected given the basic designs of the three techniques, the _ad hoc_ approach resulted in better cluster formation effectiveness; in other words, the _ad hoc_ approach appears to be more adaptable to the Mobile Node tracking task than the pre-defined clustering approaches. This higher effectiveness of the _ad hoc_ approach is at the cost of poorer load distribution though. As a result, the trade-off for higher adaptability in this case is the potential for an unbalanced depletion of resources (_i.e._, wireless node battery life) and, correspondingly, unpredictable performance of the overall network.  
In this paper, we merely focused on distributed dynamic cluster formation techniques as a solution to the reconfigurability and adaptability required of a tracking system to position/localize mobile wireless sensor nodes in factory environments. However, distributed dynamic clusters are only one component of such a tracking system. Details of network architectures and protocols as well as the multi-agent system to manage such a tracking system are to be reported in our future papers.  
Our current work in this area is focused on developing a multi-agent cluster management system to encourage maintenance by reclaiming and replacing nodes over time. For this work, we are investigating the use of homogeneous wireless sensor networks that provide minimal setup and changeover effort due to their hardware indifferences.  
Figure 12: Interval plot for tracking accuracy. \(n=160\), ANOVA, \(1\)_df_, \(p<0.05\).  
**Acknowledgments:** The authors wish to thank the AUTO21 Networks of Centres of Excellence and the Natural Sciences and Engineering Research Council of Canada for their generous support of this research.  
**Author Contributions:** The research reported in this paper is based on the first author's ([PERSON]'s) Ph.D. research. [PERSON]'s contribution is related to his supervision on [PERSON]'s research (as his PhD supervisor) and editing of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods  
### Study Area  
Guangzhou (112\({}^{\circ}\)57\({}^{\prime}\)-114\({}^{\circ}\)03\({}^{\prime}\) E, 22\({}^{\circ}\)26\({}^{\prime}\)-23\({}^{\circ}\)56\({}^{\prime}\) N) is the capital city of Guangdong Province and the largest city in southern China (Figure 1). Guangzhou contains 11 districts, including Yuexiu, Haizhu, Liwan, Tianhe, Baiyun, Huangpu, Nansha, Panyu, Huadu, Conghua, and Zengcheng. The minimum terrain altitude of Guangzhou is 0 m, the maximum is 1181 m, the mean is 107 m, and the median is 34 m. The terrain in Guangzhou is high in the northeast and low in the southwest, with mountains and hills in the north, hilly basins in the middle, and coastal alluvial plains in the south. The cultivated land is dominated by paddy fields, and rice is the main crop, with the rice planting area accounting for 88% of the total cultivated land area [41]. Other crops, such as vegetables, only account for approximately 10%. The soil type of the cultivated land is lateritic red soil (Ferralsol, World Reference Base for Soil Resources 2015), which is characterized by strong acidity and low nutrient content. The mean annual temperature (MAT) in Guangzhou is 21.9 \({}^{\circ}\)C, and MAT is higher in the south and lower in the north. The mean annual precipitation (MAP) in Guangzhou is about 1800 mm, and MAP is lower in the south and higher in the north, and the precipitation is mainly concentrated from April to September. We chose Guangzhou as the study area because, in 2018, the Guangzhou municipal government proposed to build the \"Vegetable Basket\" production and circulation service system of the Guangdong-Hong Kong-Macao Greater Bay Area [42] with Guangzhou as the hub, aiming to provide more and better edible agricultural products. However, a large amount of cultivated land in Guangzhou has been converted into built-up land due to urbanization [43]. In this context, the CLQ in Guangzhou will be a key factor in achieving the goal of providing more and better edible agricultural products to a large extent. Therefore, it is urgent and important to carry out a spatiotemporal evaluation of the CLQ in Guangzhou to promote the efficient improvement of the CLQ.  
Figure 1: Overview map of the study area. CH: Conghua district; ZC: Zengcheng district; HP: Huangpu district; NS: Nansha district; HD: Huadu district; TH: Tianhe district; BY: Baiyun district; HZ: Haizhu district; PY: Panyu district; LW: Liwan district.
## 4 Methodology  
In this work, the end goal is to compare different Machine learning architectures in order to determine which is the best suited for the Power Quality disturbances identification task. The LSTM, the CNN, CNN-LSTM and the CNN-LSTM with adjusted hyperparameters were chosen for the comparison based on their performances on previous works [4]. The LSTM was designed specifically for time series data which is well suited for this task. The CNN has proven to be very effective in image classification tasks, hence it is necessary to treat the signal as an image. Since the success of CNN is attributed to its superior multi-scale high-level image representations [27], a time frequency analysis is executed to the time series data as shown in [28] so the signal can be treated as an image. To obtain this image, the Short-Time Fourier Transform is used. It has been selected because of its simplicity of implementation but others, like the wavelet transform, can be considered as well. Data Augmentation is executed in order to obtain a more generalized result for the training and validation. The different techniques are trained with simulated data and then tested with both simulated and experimental datasets. The datasets consist of three phase voltage signals which have one sinusoidal waveform shifted 120\({}^{\circ}\) with respect to the other; both in the experimental and in the simulated datasets, PQ disturbances are superposed to the three signals. The identification algorithms are fed with 1000 samples of the time series per classification. A rectangular sliding window is used with an overlap of 500 samples.
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 6 Evaluation
### Impact of BLE Interference  
The previous measurements were conducted in a controlled environment. To put it more realistically (e.g., in a smart-home environment), external BLE and other 2.4 GHz signals outside the Bluetooth Mesh network may affect the traffic inside the mesh network. Therefore, we investigate the impact of external BLE interferences by producing various intervals of BLE beacons. In this scenario, nodes 1, 3, 7, 11, 13 act as mesh nodes, and other  
Figure 6: Comparison for the latency using a varying number of hops. The gap between the Bluetooth Mesh and ACE increased with the number of hops.  
Figure 7: Comparison for the PDR using a varying of traffic load. The ACE has significant higher PDR in high traffic load scenario.  
nodes act as BLE beacon devices broadcasting packets on all three advertisement channels at different intervals, which simulate different levels of BLE interference.  
Figure 8 summarizes the results. We notice that the PDR in Bluetooth Mesh drops with more BLE interference. Even though the PDR of ACE decreases as more interference is introduced, it generally performs better than Bluetooth Mesh. This is because, with more beacon interference, the probability of corruption due to packet collisions on three ADV channels increases, which has a negative impact on Bluetooth Mesh working only on three advertisement channels. Noting that the reason for the decrease of ACE's PDR with the same trend of Bluetooth Mesh is, node 7 happens to be scanning and advertising on advertisement channel 39 in our experiment, which can conflict with the beacon packets advertising on channel 39. However, this problem can easily avoid by selecting scanning channels among 37 data channels.
# Cyanobacterial Potential for Restoration of Loess Surfaces through Artificially Induced Biocrusts
## 2 Materials and Methods
### Ex Situ Particle Trapping and Stabilization Study
#### 2.2.2 Chl a Analysis  
The analyses of Chl a in the developed biocrusts were performed on 12 random samples from each chamber during their wet and dry state. The final concentration represents a mean value of 12 samples in each state. Using the methodology by [PERSON] et al. [54], approximately 1 cm\({}^{2}\) of biocrusts were ground in 10 mL of 100% acetone with mortar and pestle in triplicates. After extraction for 18 h at 4 \({}^{\circ}\)C, the samples were centrifuged (Tehntica, Zeleznik, Slovenia) for 10 min at 1150\(\times\)\(g\). The supernatant was separated, and the absorbances at 663 nm, 384 nm, and 490 nm were measured spectrophotometrically (SPECTROstar Nano). The contents of the Chl a were quantified according to [PERSON] [79] and [PERSON] et al. [80], using Equation (3).  
\[\text{Chl}\ a=(1.02\ \text{A}_{663}-0.027\ \text{A}_{384}-0.01\ \text{A}_{490})/ \text{C}_{\text{Chl}\ a}\ ^{*}\ \text{V}/\text{S}, \tag{3}\]  
Chl a represents the content of Chl a expressed in mg/m\({}^{2}\); C\({}_{\text{Chl}\ a}\) is the extinction coefficient of Chl a (L g\({}^{-1}\) cm\({}^{-1}\)), and its value is 92.5; A\({}_{663}\), A\({}_{490}\) and A\({}_{384}\) are the absorbances of extracts at 663 nm, 490 nm and 384 nm; V is the extract volume; S is the area of the crust sample.
## 4 Results
### Execution Time Considerations  
Sentinel's freely received high temporal and spatial resolution imagery demands big data handling, establishing computational efficiency as a key consideration. In this regard, RF and SVM classifiers are assessed in terms of both their training and implementation times. The values in Figures 11 and 12 have been averaged for five separate iterations.  
Figure 10: Kappa coefficient evolution for all levels of nomenclature using the RF classifier.  
Figure 9: Kappa coefficient evolution for all levels of nomenclature using the SVM classifier.  
RF training time and both SVM and RF implementation times exhibit a linear relationship with an increasing number of features. The two distinct steps on the RF training curve are attributed to pre-processing overhead, evident in the MATLAB implementation of the algorithm. SVM training time curve acquires linearity only for feature spaces comprising of more than 45 features. Experimenting with lower box constraint values (0.01-0.1), thus forcing the optimizer to find larger margin hyperplanes, showed that training time is both low and linearly increasing, for even smaller feature spaces (Figure 12a). However, training accuracy is compromised as the misclassification cost constraint is relaxed. Inspecting Figure 8, the threshold of 45 features coincides with the onset of the classifiers accuracy evolution plateau. Since the constraint is strict, the optimizer takes longer to find a satisfying hyperplane for spaces of fewer than 45 features. All in all, higher execution times are evident for quadratic kernel SVM classifications, with differences strengthening as the number of variables increases (Table 6).  
Figure 11: Execution time evolution with increasing number of features for the Sentinel-2 based RF crop type classification, showing in (**a**) the training time evolution and (**b**) the implementation time evolution.  
Figure 12: Execution time evolution with increasing number of features for the Sentinel-2 quadratic SVM crop type classification, showing in (**a**) the training time for three different C values and in (**b**) the implementation time for C = 1.
# FarpScun: Fully Anonymous Routing Protocol with Self-Healing Capability in Unstable Sensor Networks
## 3 Fully Anonymous Routing Protocol with Self-Healing Capability
### Performance Analysis
#### 3.5.3 Security  
(1) Man-in-the-middle attack  
We introduce the \(SP=(\mathbb{G}_{0}\),\(p\),\(\delta\)) that is generated by the system security parameters \(\lambda\), and the \(SP\) is secretly negotiated between the nodes and their neighbors. If the adversary wants to modify or delete the communication information successfully, it must obtain \(SP\).  
What is more, we introduce an additional auxiliary tool, the \(CA\) authentication center in the \(PKI\) system, to provide authentication to the public key. Digital Certificate provides a simple way to release public key. All of the participants release their public keys by applying to \(CA\) for authentication, and verify to \(CA\) that they have obtained other parties' public keys. Subsequently, both sides of communication can get each other's public keys, and they can verify each other's identity information by digital signatures.  
(2) Mutual authentication  
When the node \(i\) communicates with the node \(j\), the anonymous identity \(HIA_{ID,i\leftrightarrow j}\) can be calculated by both of the parties. If equal, the certification is successful.  
(3) Impersonation attack  
An adversary intercepts some communication contents through channel and pretends to be a legitimate user. First, the adversary does not know which two users are communicating. Second, the adversary cannot calculate the anonymous identity, authenticate, and impersonate.  
(4) Forward security and backward security  
The full communication route cannot be obtained by any node, except the destination node.
## 3 Results and Discussion
### Analysis of CIELab Parameters of Cherry Wines  
The chromatic characteristics of cherry wine with different organic acid treatments are shown in Table 4. CIELab is a color space that was defined by the International Commission on Illumination in 1976, which includes lightness (\(L^{*}\)), whose values range from 0 (black) to 100 (white), \(a^{*}\) (redness and greenness) and \(b^{*}\) (yellowness and blueness) on the hue circle. Moreover, the CIELab parameters of \(C^{*}_{ab}\), \(h^{*}_{ab}\) and \(\Delta E^{*}\) were calculated from \(a^{*}\) and \(b^{*}\), in which \(C^{*}_{ab}\) represents the saturation or intensity of color, \(h^{*}_{ab}\) refers to the relative amounts of redness and yellowness, and \(\Delta E^{*}\) stands for the total color difference between the sample and the control [37].  
In this study, the \(L^{*}\), \(b^{*}\) and \(C^{*}_{ab}\) values of wines were significantly influenced by organic acids, while the \(a^{*}\) and \(h^{*}_{ab}\) values were not changed. All groups showed an increase in \(b^{*}\) and \(C^{*}_{ab}\), which is consistent with the results provided by [PERSON] et al. [4]. The \(L^{*}\) values of the malic acid and malic-lactic acid treatment groups varied from 97.35 to 98.47, and there was a general tendency of \(L^{*}\) to increase in relation to increased acid addition, whereas high lactic acid content presented the opposite trend. Similarly, the values of \(\Delta E^{*}\) also increased in these two groups, and the overall trend was proportional to the acid addition. However, MLC-5.45 had the highest \(\Delta E^{*}\) value, which means the color difference between this group and the control group was the most obvious. The results indicated that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & \(L^{*}\) & \(a^{*}\) & \(b^{*}\) & \(C^{*}_{ab}\) & \(h_{ab}\) & \(\Delta E^{*}\) \\ \hline Control & 97.99 \(\pm\) 0.06 d & 0.84 \(\pm\) 0.72 a  & 4.75 \(\pm\) 0.59 d  & 4.87 \(\pm\) 0.42 b  & 1.39 \(\pm\) 0.18 b  & - \\ MC-3.50 & 97.98 \(\pm\) 0.10 d  & 0.53 \(\pm\) 0.08 ab  & 4.81  
the addition of organic acids has a positive effect on the yellow/blue color of cherry wine. However, the effects on color difference were not significant, because the values of \(\Delta E^{*}\) in most samples were less than one [38]. The color characteristics of fruit wine might be caused by the varying expression of anthocyanins at different pH values. Previous studies suggested that the anthocyanins are present as cis-chalcone or trans-chalcone through tautomerization and isomerization reactions under pH 3-6, featuring a yellow hue [39]. Moreover, copigmentation was recognized as an important factor affecting the color of wine during aging [40], and decreasing the pH value of the media would accelerate this process, enhancing the color of fruit wine [41].
## 4 Experimental Review  
To assess the accuracy of the numerical model and verify the performance gains of the optimized cantilever geometry, two complementary experiments were conducted. The first involved non-contact amplitude-frequency analysis using a multipath interferometry system to validate the predicted eigenfrequencies. The second experiment employed controlled base excitation to compare the electrical response of optimized and uniform cantilevers under identical loading conditions. Together, these methods provide a comprehensive evaluation of the harvester's dynamic and electromechanical behavior.  
Two types of cantilever specimens--one with a uniform cross-section and one with an optimized thickness profile--were designed for the same eigen frequency and fabricated using high-precision additive manufacturing. The components were produced on a Formlabs Form 3 L stereolithography system (Formlabs, Somerville, MA, USA), enabling an accurate reproduction of both geometries. Consistent with the numerical model, all specimens were manufactured from Engineering Resin Tough 2000, a photopolymer selected for its uniform mechanical response and reliable post-curing characteristics. The material exhibits a tensile  
Figure 10: Load resistance dependence of energy harvesting performance: (a) optimized cantilever; (b) uniform cantilever. Voltage (blue), mechanical power (green), and electrical power (red) are plotted across a resistance sweep from \(10^{2}\)\(\Omega\) to \(10^{7}\)\(\Omega\).  
modulus of 2.2 GPa, a Poisson's ratio of 0.41 [38], and a density of 1110 kg/m\({}^{3}\). A uniform cantilever beam has a constant cross-section of 0.9 mm, while the optimal shape cantilever features a thickness ranging from 0.95 mm at the clamped root to 2.14 mm at the free end, as defined by the optimized shape function. Both designs shared the same in-plane dimensions--100 mm in length and 10 mm in width--to isolate the effects of thickness shaping on vibrational behavior and energy conversion. The piezo layer was 28 \(\upmu\)m PVDF DT1-028K/L (PolyK Technologies, State College, PA, USA) stuck on to the cantilever upper layer with contacts stretching out for electrical signal transmission (Figure 11).  
The optimized cantilever geometry, as detailed in the methodology section, consists of a slightly tapered linear profile extending up to 60% of its length from the clamped end. Beyond this point, the structure transitions into a non-linearly distributed inertial mass region. In contrast, the second cantilever maintains a uniform thickness along its entire length, with no geometric variation. Despite the structural differences, both cantilevers exhibit similar fundamental eigenfrequencies. The piezo layer is the same length as cantilevers, with an 8 mm width.
# A Hybrid Tolerance Design Method for the Active Phased-Array Antenna
## 3 Statistical Analysis of the Performance of the Array Antenna  
Based on the [PERSON]'s theorem (Appendix B), the deviation \(\Delta P\) of the performance approximately obeys normal distribution, because the array antenna is composed of a large number of elements. For example, an active-phased array ballistic missile detection antenna in America has more than 2600 elements [19].  
\[\Delta P\sim N\left(\mu,\sigma^{2}\right) \tag{10}\]
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## 3 IoT-LPN Architecture and Its Applications  
The Internet of Things employs low-power and lossy networks, known as Low-Power and Lossy Networks (LLN), which may impose limits on infrastructure integration. It enables devices to interact with embedded devices, such as sensors, and can connect many nodes. The traffic variety of LLN systems is also defined; they use point-to-point, point-to-multipoint, and multipoint-to-multipoint architectures. Because of the intricacy of such a network, it is critical to have a routing protocol that serves the purpose. This has been one of the researchers' primary problems. So, to achieve this goal, the IETF ROLL working group developed RPL, a protocol for LLNs. This protocol is built on a collection-based network in which nodes gather information at regular intervals and transfer it to the collection point. The entire communication architecture is built on low-power wide area networks (LPWA) using unlicensed spectrum (Sigfox, LoRa) and other LPWA technologies proposed by the 3 rd Generation Partnership Project (3 GPP) that works within a licensed frequency range (NB-IoT, LTE-M). At the same time, the unlicensed spectrum origins made it more challenging to meet the integration goal and increased the possibility of interference and congestion. A licensed frequency range reduces external interference and improves dependability, signal-to-interference-plus-noise ratio (SINR), and security. Similarly, getting a license for these bands comes with a high upfront cost and a regular renewal price. The rise in cost will inevitably be passed on to subscribers, raising capital expenditures for deployment and ongoing operational expenses.  
Software-defined networking (SDN) architectural technology increases network performance and monitoring [26; 27]. However, the network system is divided into device management, the Internet of Everything (IoE) gateway, and intelligent LPWA with the help of AI and deep learning. IoE services provide cellular communication in the licensed and unlicensed spectrum. Similarly, AI is responsible for smart wireless communication technology using smart applications and IoE services. Some typical IoT applications developed with the help of LPWA are the smart city, track and trace, and smart building applications.