esa-sceva/satcom-chunk-collection huggingface dataset:
# Plane Detection Using Appearance-Based Pre-Processing and Convolutional Neural Network
## 3 Experiment and Performance Analysis
### Experimental Setup and Training
At this time, if the classification threshold was lowered, the TPR and FPR classified as positive in the general classification model increased together. Therefore, a curve with a higher TPR and a lower FPR on the graph can be judged as a better classification model. Likewise, PR curves are a method of evaluating the performance of classification models by changing thresholds, and the higher the precision and recall values, the better the model [31; 32]. Both the Faster R-CNN and SSD models, relatively recent deep learning-based object detection algorithms, showed little difference in accuracy, but there was a problem of SSD being superior in response to small objects in the image: detection took about 1.2 s per frame for Faster R-CNN and 0.32 s for SSD. Furthermore, with regard to the flame detection model presented in this study, it took an average of 0.38 s per frame to reach the final CNN-based inference due to low latency in exploring the ROI, and all objects assumed to be flames were extracted as an ROI. In addition, if the proposed pre-processing was applied to Faster R-CNN and SSD, the false detection rate could be decreased. However, due to the large time delay of object detection algorithms using Faster R-CNN and SSD, it is difficult to expect a rapid response when adding the pre-processing. Thus, for the models presented in this work, the responsiveness was not much different from other models, but also significantly effective in accuracy and detection rates, which is because non-flame objects are filtered in advance through feature points that are likely to be flames. Therefore, both the precision and detection rates were improved due to the high proportion of TP, while the proportion of FP was lower than that of other models presented for comparison.
## 2 Materials and Methods
### Analysis and Selection of Algorithms  
The formed datasets were divided into two subsets each. The first subset, representing 80% of the total samples, would be the training set, which was used to train various machine learning algorithms. This 80/20 split was chosen based on the Pareto principle or 80/20 rule, a common practice in machine learning. This division strikes a balance between having enough data to train robust models and retaining an adequate amount for subsequent validation [19]. During the training process, the algorithms learn patterns and relationships in the data to make predictions or decisions based on new data. The goal is for the algorithms to capture the underlying patterns in the training data and be able to generalize that knowledge to unseen data.  
Figure 3: t-SNE representation of unbalanced and balanced data in a two-dimensional space. The points represent samples from different classes. (**Left**) t-SNE projection of unbalanced data. A prominent cluster of the RG class is evident in the left side, whereas samples from the PN class are in the upper right region. The SS class exhibits weak clustering, blending with the other classes. (**Right**) t-SNE projection of balanced data in a two-dimensional space reveals improved separation and clustering of classes. The class clusters are more well-defined, although there still exist points that overlap with other classes.  
The other subset, representing the remaining 20%, was reserved for testing purposes. This dataset is used exclusively for evaluation and is not used during training. The aim of testing is to determine whether the algorithms have successfully learned and generalized without overfitting. Using a separate test set it helps detect if the algorithm has overfitted the training data, and provides a more realistic estimation of its performance.  
For the analysis, the following supervised ML algorithms were used for classification: Random Forest, Support Vector Machine, Artificial Neural Networks, Gradient Boosting, and Naive Bayes. The selection of these algorithms provides a diverse combination of classification approaches, allowing for the evaluation and comparison of their performance on the test set. This enables us to obtain a more comprehensive understanding of their classification capability, and determine which one suits best our specific problem.
## 3 Results and Discussion  
### The Existing TETRA Network  
The radioelectric coverage is calculated using the 16 TETRA base stations (Figure 6a). Table 4 gives the percentage of radioelectric coverage for each municipal area. As can be observed, the macro scalar quality is above 90% for the region and 85% for the municipal areas.  
Figure 6: Radioelectric coverage for (**a**) the TETRA system (16 sites) and (**b**) the LTE system (34 sites).
## 3 Modeling Framework
### Energy Modeling Based on the Fundamental Principle of Dynamics
#### 3.2.2 Mission Profile and Total Energy  
A delivery mission is composed of several sequential segments:  
Takeoff from the bus and vertical ascent;  
Horizontal cruise toward the delivery zone;  
Hovering and parcel drop-off;  
Return flight (cruise and descent);  
Landing back on the bus.  
Each of these phases is modeled individually with its respective speed and direction.  
The overall profile is illustrated in Figure 2, which shows a typical mission from takeoff to return.  
The total mission energy \(E_{path}\) is computed by summing the energy consumption of each segment:  
\[\begin{split} E_{\text{path}}&=\eta_{PWT}\cdot P_{ A-1}\cdot\frac{d_{v,A-1}}{v_{v}}+\eta_{PWT}\cdot P_{1-2}\cdot\frac{d_{h,1-2}}{v_{h}}+ \\ &\eta_{PWT}\cdot P_{2-B}\cdot\frac{d_{v,2-B}}{v_{v}}+\eta_{PWT} \cdot P_{B-3}\cdot\frac{d_{v,B-3}}{v_{v}}+\\ &\eta_{PWT}\cdot P_{3-4}\cdot\frac{d_{h,3-4}}{v_{h}}+\eta_{PWT} \cdot P_{4-C}\cdot\frac{d_{v,A-C}}{v_{v}}\end{split} \tag{6}\]  
This formulation provides a fine-grained and realistic estimation of the energy required for each complete delivery loop.
# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
## 7 Conclusions and Future Work  
We presented the Swahili syllabic alphabet and used it to generate word representation vectors. The study employed a convolutional neural network and a highway network to compose WEFSE. We demonstrated the quality of the word embeddings with the syllable-aware language model, achieving very competitive perplexity values that outperformed the character-aware counterpart on both small and medium datasets. We also confirmed the quality of WEFSE using the word analogy task after developing the Swahili analogy dataset. The performance of downstream NLP tasks depends on the quality of word representation. We therefore propose using WEFSE on part-of-speech tagging, machine translation and text classification tasks in future works. In addition, we will explore combining the strength of CNNs and RNNs in modeling the composition function for generating word embeddings from syllable embeddings. The need for a generic syllabic alphabet for all Bantu languages is not far-fetched and will be part of our future considerations.  
C.S.S. conceptualization, methodology, software, writing original draft, review and editing, Z.S. funding acquisition, resources, supervision, L.Q. supervision, resources, R.M. software, review and editing.  
This research was supported by the Sichuan Science and Technology Program under grants 2018 FZ0097 and 2018 GZDZX0006.  
[PERSON] is sponsored by Chinese Government Scholarship. He also appreciates the management of South Eastern Kenya University, led by [PERSON], for granting him study leave to undertake this study. We thank all the anonymous reviewers for their very useful comments and discussion.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} NLP & Natural Language Processing \\ WEFSE & Word Embeddings From Syllable Embeddings \\ CNN & Convolutional Neural Network \\ CNNs & Convolutional Neural Networks \\ RNN & Recurrent Neural Network \\ RNNs & Recurrent Neural Networks \\ LSTM & Long Short Term Memory \\ Bi-LSTM & Bi-directional Long Short Term Memory \\ \end{tabular}
# The Development of Current Collection in Micro-Tubular Solid Oxide Fuel Cells--A Review
## 8 Effect of Cell Geometry on Current Collection  
An experimental study by [PERSON] et al. [143] showed how the current density at 0.7 V of a single cell decreased by a third from 1.2 (cell a) to 0.8 A\(\cdot\)cm\({}^{-2}\) (cell b) as the distance between the anode current collector and the active region/cathode current collector increased seven-fold. They spaced four electrically isolated \"cells\" with similar areas along the 150 mm anode-supported single-chamber cell, as seen in Figure 17.  
Through impedance spectroscopy, they attributed the difference in performance to an increasing ohmic resistance with length as seen in Figure 18. They observed that the ohmic and total cell resistance followed a similar linear trend whereas the interfacial polarisation did not change significantly between cells, determining that the electrode reaction was not affected by a varying fuel utilisation and therefore changes to the performance were dominated by the ohmic resistance of the anode for these fuelling conditions.  
In 2006, [PERSON] et al. published two papers that estimated the effect of changing the anode wall thickness on cell resistance for the two geometries of \(\mu\)T-SOFC, 1.6 mm OD and 0.8 mm OD that were in development [51; 52]. They based the estimates on impedance and conductivity data they had acquired empirically for each anode at various temperatures. They conducted the study to determine whether it was sufficient to use the anode as the sole current collector--i.e., collection at one end or both ends. They concluded that an anode length of a few centimetres was the limit for such a setup due to increasing ohmic resistance. They stated that careful stack design is of paramount importance to overcome this effect. It was concluded that the anode resistance did not change linearly with wall  
Figure 17: (a) Current collector configurations and (b) cell specifications [143].  
Figure 18: Distribution of R\({}_{t}\), R\({}_{o}\) and R\({}_{p}\) of the four single cells. R\({}_{t}\), the total cell resistance; R\({}_{o}\), the ohmic resistance; R\({}_{p}\), the interfacial polarization resistance; d, the distance between the cathode of the cell and the anode current collecting point [143].  
thickness. The data for wall thickness versus cell resistance are seen in Figure 19 for the 0.8 mm OD tube.
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 2 Data and Methods
### Comparison of Ice Velocity Products
#### 3.3.2 Mean Area-Percentage of Spatial Coverage Per Scene  
In order to compare the ability to map ice speed over large regions with Landsat-8 and Sentinel-1, we compute the area-percentage of spatial coverage per scene where ice velocity is successfully tracked over Antarctica excluding the ocean. With the 29,838 Landsat-8 16-day image pairs processed, we find that on average 33% of the scene area is usable for ice speed mapping. As we only used image pairs with less than 80% cloud coverage, this estimate is even lower for the entire archive, i.e., if we include images with 100% cloud coverage. The main limiting factor for spatial coverage with Landsat is cloud cover. Indeed, we find that the mean area-percentage of spatial coverage per scene in the interior of East Antarctica for images with less than 10% cloud cover is 77, 72 and 61% for 64, 80 and 368 days repeat, respectively. This high correlation rate, even from 1 year pairs, indicates that surface features needed for the image correlation are stable over long periods of time in some regions. Sentinel-1 with 12-day pairs has a comparable success rate with a mean area-percentage of 42.2% for the 4057 scenes processed in Antarctica. For Sentinel-1, the decorrelation occurs mostly in regions with low radar backscatter, which correspond to regions with high accumulation of snowfall [38].
# Energy Efficiency of D2D Multi-User Cooperation
## 3 Analysis of System Energy Efficiency  
Different users have various requirements regarding the same service and one user has different requirements for different services. These differences lead to differences of energy consumption because of the outage probability, which is affected by requirements of QoS in terms of delay and data rate. Hence, it is necessary to take delay and data rate into account when analyzing link outage probability to examine system energy efficiency.
## 3 Definition of the Wavelet Synchrosqueezing Transform  
The SST is a time frequency analysis method. It is a special case of the reallocation method whose aim is to \"sharpen\" a time frequency representation by allocating its value to a different point in the time frequency plane [25]. This reassignment compensates for the spreading effects caused by the mother wavelet, and it is performed only in the frequency direction, thus preserving the time resolution of the signal.  
The starting point in the application of the synchrosqueezing algorithm in this paper is the continuous wavelet transform of the input signal from which instantaneous frequencies are extracted. After the extraction, an instantaneous frequency value is reassigned to a single value at the centroid of the CWT time frequency region. This final part corresponds to the squeezing of the CWT, which results in a sharpened output.  
Therefore, following the description above, the synchrosqueezing algorithm can be summarized as three main steps. The first one is the application of the CWT to the original signal \(s\), which is given by:  
\[W_{s}(a,b)=\int s(t)a^{-\frac{1}{2}}\bar{\psi}\left(\frac{t-b}{a}\right)dt, \tag{1}\]  
where \(\psi(t)\) is the mother wavelet function and the bar denotes the complex conjugate, \(a\) is the scale factor and \(b\) the translation.  
The extraction of the instantaneous frequencies from \(W_{s}\), the second step, is done using a phase transform proportional to the first derivative of the CWT. Therefore, given a phase transform \(\omega_{s}\), the instantaneous frequencies can be expressed by [25]:  
\[\omega_{s}(a,b)=-i(W_{s}(a,b)^{-1})\frac{\partial W_{s}(a,b)}{\partial b} \tag{2}\]  
Finally, the resulting wavelet coefficients containing the same instantaneous frequencies can be combined. This corresponds to the application of the SST that for a given set of wavelet coefficients \(W_{s}(a,b)\) is expressed as follows:  
\[SST(\omega_{1},b)=\sum W_{s}(a_{k},b)a^{-3/2}(\Delta a)_{k}, \tag{3}\]  
where \((\Delta a)_{k}=a_{k}-a_{k-1}\) and \(\omega_{1}\) are the frequency bins. Since the SST inherits the invertibility property of the CWT, the signal can be reconstructed. In this paper, the focus is not on the signal reconstruction.  
The WSST is applied to each of the bursts collected from the wireless devices as described in Section 4.2.
## 2 LTE
### LTE Security  
After the \"ECM connection establishment\" process between UE and eNB, the UE starts mutual authentication by transmitting IMSI to MME. Centering around the LTE security layer, the LTE network carries out mutual authentication based on EPS-AKA. The LTE security is divided broadly into three processes: UE-HSS mutual authentication process, UE-MME NAS security setup process and UE-eNB ASsecurity setup process [6, 7, 9, 14, 22, 23, 24]. LTE security process is shown in Figure 3.  
Figure 3: LTE security.
## 3 Proposed System
### Loop Closure Detection
#### 3.3.2 Similarity Measure  
To measure the similarity between images, it is necessary to convert the image into a BoW vector. To give weight to each word, we use the term frequency-inverse document frequency (_tf-idf_) [29], which is the product of the term frequency (_tf_) and the inverted document frequency (_idf_). For the image \(I_{t}\), which contains words \(w_{1},\ldots,w_{m}\), the CF attributes can be used to efficiently calculate the \(tf_{i}\) and the \(idf_{i}\) of each word \(w_{i}\) as follows:  
\[tf_{i}=\frac{n_{w_{i}}}{\sum\limits_{j=1}^{m}n_{w_{j}}}. \tag{2}\]  
\[idf_{i}=log\frac{N_{root}}{N_{w_{i}}}, \tag{3}\]  
Here, \(N_{w_{i}}\) represents the \(N\) attribute of the CF structure of word \(w_{i}\), \(N_{root}\) denotes the \(N\) attribute of the CF structure of the root node, and \(n_{w_{i}}\) indicates the number of image blocks in the image region corresponding to the word \(w_{i}\) in image \(I_{t}\). By combining the \(idf_{i}\) and \(tf_{i}\) values as the weight of each word \(w_{i}\), we can construct the BoW vector \(v_{t}\) for the image \(I_{t}\). This BoW vector is an efficient representation of the image that can be used for similarity comparison with other images.  
Once the list of matching images \(I_{t_{1}},I_{t_{2}},\ldots\), is obtained, they are converted into BoW vectors \(v_{t_{1}},v_{t_{2}},\ldots\), and the similarity with \(v_{t}\) is computed in turn. The similarity between two vectors \(v_{a}\) and \(v_{b}\) is measured by calculating the \(L1\)-_scores_\(s(v_{a},v_{b})\):  
\[s(v_{a},v_{b})=1-\frac{1}{2}|\frac{v_{a}}{|v_{a}|}-\frac{v_{b}}{|v_{b}|}|. \tag{4}\]  
These scores are then normalized with the score of \(v_{t}\) and \(v_{t-1}\), where \(v_{t-1}\) is the BoW vector of the previous image. The normalized similarity score \(\widetilde{s}(v_{t},v_  
To rank the islands, we compute a score \(H\) for each of them. The score is computed using the function \(H(v_{t},V_{t_{i}})\):  
\[H(v_{t},V_{t_{i}})=\sum_{j=n_{i}}^{m_{i}}\widetilde{s}(v_{t},v_{t_{j}}), \tag{6}\]  
where \(V_{t_{i}}\) represents the set of all BoW vectors in the island \(\mathrm{Y}_{t_{i}}\).  
After selecting the island \(\mathrm{Y}_{t_{j}}\) with the highest score \(H(v_{t},V_{t_{j}})\) as the matching group, the loop closure detection process continues to the temporal consistency step. Grouping similar images into islands helps to narrow down the search space and focus on finding the best match within a smaller subset, which improves the accuracy and efficiency of the loop closure detection process.
## 2 Problem Statement
### Comparison of the Real-Time Throughput  
Figure 3 shows the real-time throughput comparison of SCTP and TCP under different PERs in networks that combine wire-wireless and multi-hop situations. The PERs were selected as 0%, 10%, 20%, 30%, 40%, and 50%, respectively.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Wireless protocol & IEEE 802.11 \\ Time slot & 20 us \\ Wireless bandwidth & 1 MBps \\ Wired bandwidth & 5 MBps \\ Link delay & 2 ms \\ SCTP maximum transmission unit (MTU) & 1500 B \\ SCTP chunk size & 1468 B \\ Simulation time & 600 s \\ Error rate type & Packet error rate \\ Rate values & 0\%–50\% \\ \hline \end{tabular}
\end{table}
Table 1: Parameters Settings.  
Figure 2: Comparison of average throughput between SCTP and TCP.  
Figure 3a shows when the PER was zero, the throughputs of SCTP and TCP were 90 KBps and 77 KBps separately during the 600 s simulation time. The real-time throughput of SCTP was greater than TCP. In Figure 3f, when the PER was 50%, the throughputs of TCP and SCTP were both less than 4 KBps, and the throughput of the SCTP was always higher than TCP during the simulation time. When the PER was 10%, the average throughput of SCTP was 50 KBps, while the average throughput of TCP was 30 KBps in Figure 3c. On the whole, the real-time throughput of SCTP was higher than TCP. However, with the increment of PER, the advantages of SCTP became smaller. In Figure 3e, when the  
Figure 3: Real-time throughput comparison when the error rate is 0–50%. (**a**) Packet error rate (PER): 0%; (**b**) PER: 10%; (**c**) PER: 20%; (**d**) PER: 30%; (**e**) PER: 40%; (**f**) PER: 50%.  
PER reached 40%, the throughputs of SCTP and TCP were close to zero. When the data PER was higher than 10%, the real-time throughput of TCP showed a significant trend of increasing at the beginning of data transmission in Figure 3. Moreover, the real-time throughput of TCP was much larger than SCTP, which could be explained by the connection mechanism of TCP and SCTP. SCTP required four handshakes to establish a coupling when establishing a connection. However, TCP required only three handshakes to establish a connection. The connection process of SCTP was more complex and took more time than TCP, therefore TCP had a higher throughput than SCTP when data started to transmit.
# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
## 3 Prototype of 5G Integrated with TSN
### Measurement Setup for Time Synchronization  
For a precise and deterministic transmission of the messages, and for reliable measurements of the prototype performance, the synchronization between the different components is important, especially for the over-the-air performance validation. Therefore, a Pulse-Per-Second (PPS) signal is being used to compare the definition of time of each device. Using PPS signals, an offset between two or more clocks can be measured. For that, a Grand-Master Clock, with a PPS interface, is introduced to the system as a synchronization source. Furthermore, the TSN switch on the User-Equipment side of the 5G system is extended by  
Figure 6: Latency histogram of 100,000 samples on the URLLC testbed with UDP traffic with a payload of 1420 bytes.  
Figure 7: Integration of the 5G-URLLC system in the TSN based network.  
a PPS interface. Connecting the PPS interfaces of the switch and the Grand-Master Clock to an FPGA (Field Programmable Gate Array) based PPS Analyzer from NetTimeLogic allows us to measure the over-the-air time synchronization with a resolution of 4 ns. The overall hardware architecture with the PPS measurement setup is shown in Figure 8.
## 3 Results and Discussion
### Effect of Drying on Cassava Leaves Quality  
#### 3.2.1 Quality of Fresh Cassava Leaves  
The chemical composition of fresh cassava leaves varied depending on the environmental conditions, such as rainfall and temperature, on different harvest dates. On average, fresh samples contained 72.34 \(\pm\) 0.89% w.b. of moisture content, 35.02 \(\pm\) 1.78 g/100 g d.b. of crude proteins, 971.25 \(\pm\) 197.45 mg/100 g d.b. of vitamin C, 136.84 \(\pm\) 5.68 mg/100 g d.b. of \(\beta\)-carotene, 81.62 \(\pm\) 3.70 mg/100 g d.b. of lutein, 542.90 \(\pm\) 32.62 mg/100 g d.b. of chlorophyll-\(a\), 253.74 \(\pm\) 13.59 mg/100 g d.b. of chlorophyll-\(b\), and 1315.97 \(\pm\) 141.64 mg/kg d.b. of total cyanide. The average amount of 18 amino acids in fresh cassava leaves is shown in Figure 2, indicating that the major amino acids were glutamine (3.87 \(\pm\) 0.27 g/100 g d.b.), aspartic acid (3.50 \(\pm\) 0.16 g/100 g d.b.), and leucine (3.17 \(\pm\) 0.21 g/100 g d.b.), with lower amounts of cysteine, methionine, tryptophan, and histidine.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Temperature**} & \multicolumn{2}{c}{**Parameters**} & \multicolumn{2}{c}{**R2**} & \multicolumn{1}{c}{**MAPE**} & \multicolumn{1}{c}{**RMSE**} \\ \cline{3-8}  & & (\({}^{\circ}\)C) & **k** & **a** & **n, g, b** & & & \\ \hline \multirow{6}{*}{MR = a\(\cdot\)exp(\(-\)kt) + (1 \(-\) a)exp(\(-\)gt)} & 40 & 0.1435 & 5.9405 & 0.1316 & 0.9967 & 1.9392 & 0.0026 \\  & 50 & 0.2653 & 7.9531 & 0.2437 & 0.9976 & 1.7214 & 0.0039 \\  & 60 & 0.4698 & 10.2358 & 0.4469 & 0.9945 & 7.7550 & 0.0065 \\  & 70 & 0.6640 & 9.6509 & 0.6225 & 0.9965 & 2.2694 & 0.0078 \\  & 80 & 3.3682 & 23.6361 & 3.4944 & 0.9998 & 3.5129 & 0.0021 \\ \hline \multirow{6}{*}{Diffusion approximation MR = a\(\cdot\)exp(\(-\)kt) + (1 \(-\) a)exp(\(-\)kbt)} & 40 & 0.3481 & 5.1768 & 1.1411 & 0.9984 & 0.6725 & 0.0018 \\  & 50 & 0.2731 & 4.7597 & 0.8614 & 0.9976 & 1.7214 & 0.0039 \\ \cline{1-1}  & 60 & 0.4800 & 5.6582 & 0.9101 & 0.9945 & 7.7364 & 0.0065 \\ \cline{1-1}  & 70 & 0.6812 & 5.5214 & 0.8887 & 0.9965 & 2.2555 & 0.0078 \\ \cline{1-1}  & 80 & 0.3481 & 5.1768 & 1.1411 & 0.9997 & 3.3528 & 0.0024 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Amino acid profile of fresh cassava leaves.