esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Results
### Hotel Recommendation Service Test
As discussed before, the closeness of CI to l (the higher value of CI) shows a greater chance for selection of candidate hotels. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cluster**} & **C1** & **C2** & **C3** & **C4** & **C5** & \\ \cline{2-6}  & **Average** & **Average** & **Average** & **Average** & **Average** & **CI** \\ \hline
**Cluster 1** & 4263.399 & 2660.783 & 1193.158 & 1.125 & 1.875 & 0.449633 \\
**Cluster 2** & 727.4722 & 963.8861 & 1344.764 & 2 & 3.3 & 0.767236 \\
**Cluster 3** & 1119.911 & 1038.127 & 2221.861 & 1 & 1.4 & 0.625212 \\
**Cluster 4** & 4384.46 & 2299.778 & 3088.15 & 3 & 4 & 0.326147 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cluster agent comparison using TOPSIS: Claverage, C2 average, and C3 average represent the average distances of hotels from the closest fast food restaurant, subway, and park in the metric units for the cluster agents, respectively; C4 average and C5 average represent the average cost and number of stars for the cluster agents, respectively, while CI is the closeness index. Figure 9: TOPSIS score results. The hotel with the highest value of CI is selected as the final hotel. The mentioned result shows that the result of Figure 9 is the result of the system for a search radius (here, 2000 m) that generates a small number of candidate hotels but the results of Tables 4 and 5 are for managing the largedata set, which here is using all candidate hotels for the analysis.
## 4 Simulation Results
The bus voltage during each of the two simulations is shown in Figure 7. While the bus voltage varies more when optimal high dimension droop control is used, it stays well within a bound of 5% around the reference value of 300 V.
## 3 Data Collection and Observation Quality Assessment  
The experiment was conducted on the roof of the main teaching building at Chang'an University, located in Xi'an City, Shaanxi Province. The experimental environment had an uncovered view and favorable observation conditions, which are shown in Figure 2. The experiment equipment was conducted using Huawei Mate40, Huawei P40 and No-vatel receiver named DH04, and the smartphone was set to upright. The received signal frequencies of the two smartphones are shown in Table 2. The Huawei Mate40 and P40 can receive GPS L1 + L5 dual frequency, GLONASS L1C, BDS B1I, Galileo E5a and QZSS L1 + L5 dual-frequency signals. Furthermore, in real-time processing, we received the real-time stream of multi-GNSS broadcast ephemeris and satellite orbit and clock offset products from the NTRIP Caster. The experiment was conducted on 16 November 2022 at a sampling rate of 1 s and a total duration of approximately 2 h from 10:00 to 12:00 UTC, with the use of Geo++RINEX Logger. The mean coordinates of the last 500 s were used as the reference position.
# End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
## 2 Materials and Methods  
To decipher and categorize these radargrams, we first reduce the noise in the radar images. Then, we apply the proposed network to robustly classify these ice subsurface targets.  
The proposed network structure is shown in Figure 2. Images are pre-processed with bilateral filtering [22], which reduces the amount of noise interference. The network is comprised of an encoder and a decoder. The encoder, which includes the network backbone and the ASPP module, extracts radar image features. The ResNet network, which includes atrous convolution, forms the network backbone, and captures long-range information without changing the image resolution. The decoder combines the features extracted by the encoder with the low-level features. We then apply two 3*3 convolutions to refine the features, follow by a simple bilinear upsampling by a factor of 4. We adopt an improved ASPP structure. By removing image-level features, we change the five branches structure into a four branches structure. Additional input feature dimension compression during convolution removes redundant information. To reduce the training time, we modify the channel combination of the decoder by changing the original channel combination from (256,48) to (64,16). The improved network achieves end-to-end processing, and results in faster, more accurate classification of the ice sheet subsurface targets in radar images.
# Querying on Federated Sensor Networks
## 4 Results
### The Comparison of the Layered and Flat FSN Models
#### 4.2.1 The Success Rate Results  
We observe that both L-FSN and Directed Diffusion protocols support querying distances of 10 building hops with a 100% success rate, as demonstrated in Figure 4.  
Querying protocols evaluated for the flat FSN model suffer from various characteristics of FSN deployment. In this experiment, we observe that both Rumor Routing and DQT cannot support querying up to a distance of 10 building hops. Rumor Routing suffers from communication loops on the flat FSN model due to random forwarding of packets that have a limited _time to live_ period. Instead of flat management, a layered management of FSN alleviates occurrence of packet  
Figure 4: Query success percentage.  
forwarding loops by bounding the packet forwarding area into islands. On the flat FSN model, the location-based infrastructure of DQT cannot support querying when data source is found in another island. The reason is that, when DQT is built over the entire FSN topology, a geographical clusterhead cell may not include a node due to communication holes on the sparsely-connected FSN deployment. Therefore, location-constraint protocols are not directly applicable to the entire FSN using the flat FSN model.  
GHT suffers from geographical forwarding. The geographical forwarding method directs a packet to the closest neighbor of a destination location. If none of the neighbor nodes are found to be closer, a packet cannot be forwarded to a destination. This is why some of the query packets are not resolved in FSN and this situation is observed with an oscillation of the success rate data of GHT for values between seven and 10 building hops in Figure 4. Therefore, querying protocols using geographical forwarding require an efficient reliability mechanism to guarantee packet delivery on a sparsely-connected flat FSN model.
# Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
## 3 Proposed Method SOC Estimation
### Long Short-Term Memory  
Unlike the general MNN model, LSTM is a method that can take time and order into account as the result of the hidden layer is, once again, used as an input value of the hidden layer. In addition, the LSTM devised by adding cell state to solve the gradient vanishing problem of long-term dependency of the MNN model [31]. It is often used for predicting time series data, and if the data used in this paper is used, more accurate results can be obtained than with the MNN model.  
In this paper, we used Long Short-Term Memory (LSTM). The input layers are 2, 6, 8 and 3 and 7 are used by adding temperature variable data. There were three hidden layers, and all the LSTM layers were used. In the first layer, there were 150 nodes; in the second layer, 100 nodes; and in the third layer, 50 nodes. The ReLU function was employed as the activation function of the three LSTM layers. In the output layer, there were 101 nodes, and the Softmax function was employed as the activation function. Adam was used as the learning method.  
LSTM is one of the useful types of the RNN that can calculate the slope value of the basic RNN. Figure 4. presents the basic structure of LSTM. LSTM is a suitable method for SOC estimation as it considers the decrease in battery capacity as a time series [18]. The equation of LSTM is as follows.  
Step 1. Forget gate:  
\[f_{n}=\sigma\big{(}W_{f}\cdot[h_{n-1},X_{n}]+b_{f}\big{)} \tag{7}\]  
Step 2. Input gate:  
\[i_{n}=\sigma(W_{i}\cdot[h_{n-1},X_{n}]+b_{i}) \tag{8}\]  
\[\widetilde{S_{n}}=tanh(W_{c}\cdot[h_{n-1},X_{n}]+b_{c} \tag{9}\]  
Step 3. Update(Cell state):  
\[S_{n}=f_{n}\circ S_{n-1}+i_{n}\circ\widetilde{S_{n}} \tag{10}\]  
Step 4. Output gate(Hidden state):  
\[O_{n}=\sigma(W_{o}[h_{n-1},X_{n}])+b_{o} \tag{11}\]  
\[h_{n}=O_{n}tanh(S_{n}) \tag{12}\]  
where, \(W\) is the weight; \(h\) is the hidden state; \(X\) is the input; \(b\) is the bias of each gate and state; \(f\) is the forget gate; \(i\), the input gate; \(S\), the cell state; and \(O\), the output gate.  
Softmax is a activation function that normalizes the output value in the last step for class classification with respect to the output value. The sum of the resulting values of the Softmax function is always 1 [32]. The equation of Softmax is as follows,  
\[\text{f}(x_{i})=\frac{e^{x_{i}}}{\sum_{j=1}^{N}e^{x_{j}}} \tag{13}\]  
where \(x_{i}\) is the \(i\)-th output, and \(N\) is the number of dimensions equal to the number of classes [33].
## 4 Experimental Results
### Action Diversity Evaluation  
To assess the adaptability of our trained agent, we evaluated the performance of our best-performing policy from TD3 + CER using three different decision-making modules. Each decision-making module generated distinct sub-goals within the same scenario, allowing us to examine how effectively the motion planner responded to these sub-goals.  
Figure 14 illustrates the vehicle trajectory influenced by three distinct decision-making modules, representing different driving styles: original (blue), aggressive (red), and tolerant (yellow). The variation in time-to-collision thresholds resulted in different behaviors within identical situations. Table 1 displays the thresholds for each DM module, denoted by \(TTC_{\text{front}}\) and \(TTC_{\text{rear}}\). Figure 14 presents the trajectories of each vehicle with line markers and highlights the predetermined guidance lanes, represented by discrete gray areas every 800 m. The original decision-making module reflects the approach used during training. The \"tolerant\" module employed higher thresholds for lane changes, while the \"aggressive\" module exhibited a propensity for frequent lane changes. These DM modules and the associated maneuver analyses were evaluated under identical conditions on a four-lane highway with 50 nearby vehicles. Table 2 presents an analysis of the trajectories generated by each DM module using an identical motion planner, including the number of lane changes (\(N_{\text{LC}}\)) and the average velocity (\(v_{\text{avg}}\)).  
Figure 14 depicts the velocities of each vehicle within the preferred speed range of 20 to 30 m/s. The \"aggressive\" approach involved frequent lane changes to maintain speed, particularly when encountering slower vehicles ahead. Conversely, the \"tolerant\" approach strictly adhered to the guidance, sometimes even decelerating. The dashed gray line in Figure 14 represents the desired speed limit of 30 m/s. In instances where no vehicle was in front, such as at 100 m in Figure 14, the vehicles accelerated close to the desired 30 m/s. Our best-performing policy demonstrated adaptive behavior, generating diverse actions within the same scenarios based on the provided sub-goals.  
Figure 12: Sub-goals and resulting paths of each agent are depicted in the figure. The yellow line represents the sub-goal decisions, labeled as “left”, “right”, and “idle”. As the guidance lane changes at 1000 m, mandatory lane changes must occur for both agents in **(a,b)**. While a lane change occurs in **(a)**, it does not take place in **(b)**.  
Figure 13: Case study of overtaking performance: Section from 1100 to 1470 m in Scenario 3. The stability of the lane-changing maneuver is shown in **(a)**. Examining the relative maneuver of the ego vehicle to the obstacle vehicle during overtaking, illustrated in **(b)**, the ego vehicle maintains a safe gap, ensuring a safe overtaking maneuver.
## 2 Public Safety Network
### Drones and IoT for Public Safety  
Recently, drones have played a vital role in developing the next generation of wireless network and IoT. Therefore, they have attracted the attention of people who want to quickly develop drone applications that serve society, industry, government, and the environment. Furthermore, drones have a branch of sensors that can get real-time information everywhere they fly. Recently, a lot of connected sensors are being efficiently replaced by one drone that has the most essential features such as ease of deployment, flexible payload, programmability, and the ability to measure anything at any time anywhere. Drones can enable communication services while wireless communication networks are damaged during disaster [19]. They can also be used to gather information at a particular place and send the information to a control center in real time to aid with making the right decisions.  
Drones offer several advantages over IoT stationary devices such as: they can be used for lengthy periods at any altitude, they have better resolution image quality, are low cost, have a rapid response, are capable of flying in any conditions, and are able to get closer to areas of interest. They represent a useful emerging technique for delivering wireless service to users or IoT devices on the ground, and have been investigated for their large coverage area and reduced power consumption [70].
###### Abstract  
The purpose of the study is to identify variations in wellness parameters among elite female athletes, considering simultaneously the influence of natural menstrual cycle (MC) or hormonal contraception (HC) phases and weekly and seasonal variations. A total of 54 elite female athletes were followed up with through a 6-month daily monitoring of their reported fitness, sleep quality, mood, and menstrual symptoms. Estimated cycle phases, days of the week, and seasons were all integrated into a single model to study cyclicities concomitantly. A total of 263 cycles were analyzed, totaling 7370 pieces of daily self-reported data. We observed more frequent symptoms during the menstruation and the pause phase. Reported fitness was greater in the mid-cycle and mood was lower at the end of the cycle for the MC group. Wellness parameters were rather stable for HC users. For both groups, symptoms were more frequent in summer. Reported fitness levels were lower on Monday, while mood scores were highest on Saturday and Sunday. In summer and autumn, fitness, sleep quality, and mood were greater. A cyclical wellness rhythm was identified in the menstrual cycle and persists even when considering weekdays and seasons cyclicities. This comprehensive approach helps to identify physiological influence on human behaviors, considering the specificities of women.  
Keywords:cycles; female athletes; monitoring; performance; wellness +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### Chemicals and Samples  
The microalga _I. galbana_ was selected for this research. Mexican Company \"Microalgas Oleas de Mexico S.A.\" (Guadalajara, Mexico) kindly donated the dry biomass for this research. The microalga was cultured under a controlled condition and harvested in its exponential growth phase. The biomass was dried under the freeze-dry system such as Labconco FreeZone 2.5 L Benchtop Dry System (Labconco, Kansas City, MO, USA), packed in vacuum sealing plastic bags, and stored at 4 \(\pm\) 2 \({}^{\circ}\)C in darkness until use. The chemical materials used for supercritical fluid extraction (SFE) were carbon dioxide (99% purity), purchased from Indura Group Air Products (Santiago, Chile) and ethanol (99.5%), from Merck (Darmstadt, Germany). Other chemicals such as ultrapure water, fucoxanthin standard, gallic acid, 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox, \(\geq\) 97%), 2,2-azino-bis (3-ethylbenzothazoline-6-sulfonic acid (ABTS \(\geq\) 99%), and Folin-Ciocalteu phenol reagent were purchased from Sigma-Aldrich (Santiago, Chile). Chromatographic grade ethyl acetate, water, acetonitrile, methanol, and n-hexane were purchased from Sigma-Aldrich (Santiago, Chile). For fatty acid identification and quantification, a standard fatty acid methyl ester (FAME) mix, C4-C24, supplied by Supelco Analytical (Bellefonte, PA, USA) was used, and tripentadecain > 99% (Nu-Check Pre, Inc., Elysian, MN, USA) was used as the internal standard.
## 2 Passive Wake-Up Radio  
The growth in IoT draws massive interest in the use of RF energy harvesting in making self-sustainable devices realistic [6]. Electromagnetic energy from the RF source is utilized to feed an electronic device remotely [25]. RF harvesting wake-up radios utilize RF energy as their power source. During RF energy harvesting, electromagnetic waves are converted into electricity using a rectifyingantenna, or rectenna. The energy is transmitted in the form of electromagnetic radiation through a radio signals medium with a frequency range from 3 kHz to as high as 300 GHz.  
In this section, we present the configuration in the passive wake-up radio hardware architecture and the RF energy transfer and harvesting techniques. We first explained the passive wake-up radio design and the circuit used for the RF-based energy harvesting in wake-up radios. We then describe the state-of-the-art in the RF energy transfer and harvesting techniques and finally reviewed the existing passive wake-up radio architecture available in the literature.
## 4 Theoretical and Experimental Analyses
### Embedding Rate  
The ER represents the average amount of additional data embedded per pixel. The embedding method of this scheme is polynomial redundancy embedding, which is related only to the size of the carrier and the choice of parameters. It can be calculated as follows:  
\[ER=\frac{8\times(k-1)}{n} \tag{16}\]  
It can be seen that the proposed scheme has a more stable embedding performance because the ER is only related to the parameters \(k\) and \(n\). Table 3 demonstrates the maximum ER of our scheme under the different common thresholds, from which it can be seen that the ER is independent of the value of \(h\) when \(k\) and \(n\) are certain, and the ER is better when \(n\)-\(k\) is smaller. To demonstrate the superior embedding performance of the proposed scheme, Figure 12 shows the comparison results of the ER between the proposed scheme and similar state-of-the-art schemes. The ER of the proposed scheme is the highest of all five test images in the scheme. Moreover, it has the greatest ER in the test image except for the Airplane under the common \((2\to 2,2)\) and \((3\to 4,4)\) schemes, because the Airplane is relatively homogeneous, a large number of redundant pixels can be compressed and extra data can be embedded. The comparison schemes are all based on secret sharing, when \(k=n\), those schemes are not disaster-tolerant. Therefore, the scheme has a wider range of applications when \(k\
eq n\). In this case, our scheme also has better embedding performance. Since this proposed scheme makes full use of the binary polynomial coefficient redundancy generated in the encryption process, the scheme has a larger ER and a more stable embedding performance.
## 2 Explanation Methods
[14] introduced a model diagnostic system called Anchor. The Anchor model utilizes a strategy that is based on the perturbation technique to generate local explanations for the ML model outputs. Therefore, instead of the linear regression models implemented by the LIME method, the Anchor explanations are presented as IF-THEN rules that are easy to understand. Anchors adopt the concept of coverage to state exactly which other and possibly unseen instances they apply to. Anchors are determined by methods of an exploration or multi-armed bandit problem, which has its roots in the discipline of reinforcement learning. Several ML models, Logistic Regression (LR), Gradient-Boosted trees (GB), and Multilayer Perceptron (MLP), were explained in this paper to show the flexibility of the Anchor method. [PERSON] et al. [15] proposed an agnostic method called LORE which provides interpretable and faithful explanations of the ML models. Firstly, a genetic algorithm is used to generate a synthetic neighborhood around the intended instance to allow LORE to learn a local explainable predictor. Then, LORE exploits the concept of the local explainable predictor to derive significant explanations, including decision rules that explain the causes behind the decision and counterfactual rules. These rules present the fluctuations in the features of inputs that lead to a different prediction. SVM, RF, and MLP ML models were explained, in this work, using LORE on three datasets (adult, German, and compass).
## 2 Materials and Methods
### Data Statistical Analysis  
We used one-way ANOVA to compare the differences in various VOCs at different stages. Additionally, principal component analysis (PCA) and hierarchical clustering analysis (HCA) were employed to assess the differences in floral scent characteristics among samples at different stages. One-way ANOVA and PCA were performed using built-in functions in R version 4.4.1 [24]. HCA and heatmaps were generated using the \"complexheatmap\" package [25]. To further analyze the release patterns of different VOCs at various stages, K-means clustering analyses were utilized, and then performed using the stats 4.2.0 package in R [24]. To identify VOCs with the most significant differences in release at different stages, we used partial least squares-discriminant analysis (PLS-DA) and evaluated each VOC's contribution to differentiating floral scent characteristics across stages using variable importance in projection (VIP) values. A VOC with a VIP > 1 was considered to have a very significant difference in release across different stages. PLS-DA and VIP values were calculated using the \"ropls\" package [26]. All of these analyses were conducted based on the VOCs' concentration data normalized with internal standards.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Attack Attempts  
#### 4.7.1 External Adversary  
For an external adversary acting as a man in the middle, manipulating messages, or sending newly created messages to selected nodes, the attack's success (accepting the tag) depends on a victim node. Because an external adversary does not know the key \(K\) and the MAC algorithm can be modeled as a random oracle for a good MAC function, the probability of forging a valid \(T_{2}\) is \(2^{-v}\).  
Considering the probability of impersonating the sender in front of a _single_ receiver, in order for the adversary to succeed they must forge \(T_{2}\) as above; notably, they mustonly guess some bits of \(T_{1}\), not all of them. Specifically, note that each receiver \(a\in\mathcal{R}\) only belongs to a certain number of sets from the separating family \(\mathcal{SF}=\{F_{1},\ldots,F_{\ell}\}\) associated with the multicast group (this is the key point in the construction that introduces sender-receiver asymmetry). This translates to the receiver being able to check only some bits of \(T_{1}\). Bits corresponding to sets from \(\mathcal{SF}\) that the receiver does not belong to are essentially random from its point of view, and cannot be checked for validity. Specifically, the number of bits of \(T_{1}\) that can be verified by a node \(a\) equals  
\[v_{a}=\sum_{\begin{subarray}{c}i=1\\ a\in F_{i}\end{subarray}}^{\ell}u_{i}\,.\]  
Therefore, the overall probability of node \(a\) accepting a forged tag is \(2^{-(v+v_{a})}\). As can be seen, there is a tradeoff between resilience against internal and external adversaries. For example, if \(u_{i}=u\) for each \(i\), then the separating family (5) enables each receiver to verify \(3u\) bits out of \(5u\) bits of \(T_{1}\). In the case of the separating family (7), a receiver can verify \(2u\) out of \(5u\) bits, and the false acceptance probability increases from \(2^{-(v+3u)}\) to \(2^{-(v+2u)}\). This shows that not only the cardinality but also the specific choice of a separating family makes a difference to security.