esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Return Loss Before and After Ablation  
Return loss was measured using a VNA before and after an ablation procedure, as described in Section 2.7. This was conducted to ascertain any correlation between changes in return loss due to expansion. Mean return loss and standard deviation at 2.45 GHz for both annealed (\(\overline{\rm X}_{\rm RWA}\) and \(\sigma_{\rm RWA}\)) and non-annealed (\(\overline{\rm X}_{\rm RWN}\) and \(\sigma_{\rm RWN}\)) antennas were calculated and plotted in Figure 7 to visually illustrate the changes. Before the experiment, \(\overline{\rm X}_{\rm RWA}\) and \(\sigma_{\rm RWA}\) were 16.96 dB and 1.68 dB, respectively, whilst \(\overline{\rm X}_{\rm RWN}\) and \(\sigma_{\rm RWN}\) were 18.82 dB and 1.88 dB, respectively. After the experiment, \(\overline{\rm X}_{\rm RWA}\) and \(\sigma_{\rm RWA}\) changed to 11.25 dB and 2.82 dB, respectively, whilst \(\overline{\rm X}_{\rm RWN}\) and \(\sigma_{\rm RWN}\) changed to 6.66 dB and 0.23 dB, respectively. The antennas were all developed under similar conditions; thus, the initial return loss was understandably similar. However, there was a significant difference after ablation. Although the annealed antennas experienced a change in return loss of 5.67 dB, the non-annealed antennas had a change of 12.16 dB. This signifies that the expansion occurring during ablation significantly degrades antenna return loss. In addition, the annealing process reduces this effect.
# A Location Routing Problem with Time Windows Consideration: A Metaheuristics Approach
## 2 Materials and Methods
### Proposed Method  
[PERSON] and [PERSON] [42] proposed an algorithm that can minimize the probability of tumbling into local optima traps by systematically alter the surrounding area during the local search. This results in the creation of a metaheuristic that is known as variable neighborhood search (VNS), which has been further developed through its many extensions. Variable neighborhood search (VNS) has been recognized by its ability to solve various types of problems, especially the vehicle routing problem and its extensions [43; 44; 45; 46; 47; 48]. VNS also has been proven to solve several cases of LRP including CLRP [49], inventory LRP [50], multi-echelon LRP [51], and periodic LRP [52]. The amended and intense VNS, i.e., variable neighborhood descent (VND), was employed by [53; 54; 55] to take advantage of different neighborhoods structures for the vehicle routing problem.  
This section presents a variable neighborhood search with a path relinking (PR) algorithm developed for solving CLRP as used by [PERSON] et al. [39]. The proposed VNSPR algorithm is explained in detail in the subsequent sections, including the general structure, solution representation, initial solution, different kinds of neighborhoods, and the detailed method.
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 2 The Cosmo ArduSiPM Architecture
### Microcontroller Selection  
Our strategy for selecting microcontrollers is centered on two key factors: the processing power of the chip and the availability of both analog and digital peripherals crucial for our project's specific needs. This approach allows us to start with a standard, commercially available chip and, if necessary, upgrade to a version that can handle radiation. Such flexibility is indispensable for the evolving needs of space missions, where both cost and performance are critically balanced.  
Commercially available microcontrollers, while cost-effective and well-tested, often fall short of the high standards required for space missions, especially in terms of radiation resistance. Assessing whether these standard chips can endure space conditions is not only a detailed but also an expensive process.  
In response to these challenges, we adopted the Microchip SAM S series of microcontrollers, including the Microchip SAMV71 (Figure 3), known for their high-performance processors capable of handling tasks swiftly and precisely. These chips can operate at speeds up to 300 MHz and are equipped with substantial memory capacity, suitable for tasks such as creating histograms for data analysis. Their versatility is further enhanced by various connection options and features that make them adaptable to different applications.  
For missions that orbit close to Earth, where radiation levels are comparatively lower, we specifically use the SAMV71Q21 RT (Microchip Technology Inc., Chandler, AZ, USA) [19]. This special version of the SAMV71 is designed to resist radiation, making it ideal for such environments. It is immune to latch-up (Single-Event Latch-up (SEL) > 60 MeV) and has undergone rigorous testing for Single-Event Upset (SEU) and Total Ionizing Dose (TID) [20]. This ensures that our devices operate reliably in space without radiation damage, meeting the stringent quality standards required for space equipment.  
Furthermore, this radiation-resistant model maintains the same design and pinout as the standard versions, allowing us to use the same printed circuit board (PCB) for different versions of our devices. This uniformity not only simplifies manufacturing but also ensures consistency across our designs. Importantly, the chip is available in small quantities, making it viable for smaller projects and supporting our commitment to cost-effectiveness and scalability.  
Figure 2: Block diagram of Cosmo ArduSiPM. The implementation of most functions relies on the microcontroller’s internal peripherals. Depending on the specific application, the analysis and interfaces can be configured accordingly.
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Flying Taxis Benefit People Who Need to Travel for Business Purposes  
Flying taxis can benefit people who need to travel for business purposes in several ways. Firstly, they offer a faster and more efficient way to travel compared to ground transportation, enabling business travelers to get to their destinations quicker and potentially saving them time and money. Additionally, flying taxis can provide access to areas that are not easily accessible by traditional means of transportation, which could be particularly useful for business travelers who need to reach remote locations or areas with limited ground transportation options. Finally, flying taxis can offer a more comfortable and productive travel experience for business travelers, providing them with a quiet and private environment to work or rest during their journey [167].
## 3 System Description  
Figure 1 illustrates the considered WPSN, which includes one HAP and a number of sensor nodes in a cell. The HAP is a power source that wirelessly transfers energy to all of the sensor nodes and also acts as a sink node that collects sensing information in the cell. Only the HAP has a stable energy supply, but the sensors do not have any embedded energy source or battery. The HAP periodically broadcasts an RF wave, and all of the nodes in the cell harvest energy from this RF signal. Considering the hierarchical structure, the sensor nodes are grouped into a number of clusters and one CH is selected from among the member nodes in each cluster. Thus, all of the sensor nodes send sensing data to their CH instead of directly to the HAP. The CH aggregates all of the sensing information received from its member sensor nodes as well as its own sensing information (i.e., data fusion), then sends the aggregated data to the HAP at once.  
Figure 1: Considered wireless powered sensor network.  
Assuming that all of the sensors perform the same sensing task, such as temperature, humidity, or fire sensing, the bit size of the sensing data transmitted by each sensor node is the same. However, the sensor nodes inherently harvest different amounts of energy depending on their distances from the HAP and consume different amounts of energy for transmission depending on their distances from the CH. Incidentally, the maximum rate of sensing data collection in the WSN is limited by the worst sensor node, which has a low harvesting energy and poor link budget [19; 20]. Therefore, all of the sensor nodes only need to support the same data rate as the worst node so that some sensor nodes may have energy remaining after transmitting their sensing data to the CH. Since the CH usually requires more energy for multiple receptions, processing, and uplink transmission to the HAP, the sensor nodes transfer the remaining energy to the CH while sending their data by using the SWIPT technique, and the CH acquires additional energy while receiving data from its member nodes.
## 4 Implementation and Operations
### Customized WSN Services  
When an external sensor application issues a request, it will send a request message to _AppManager_ as follows:  
[OP] [OP_ARGUMENT]* (* means 0-many)  
Figure 8: Processing of a URGENT request which accesses to a specific sensor node.  
The [OP] field indicates a requested operation which _Slice_ will serve, such as calculating the maximum value of sensing data. The [OP_ARGUMENT] fields are variable depending on the [OP] field. When the request is arrived at the server socket of _AppManager_, it delivers the request to _SliceManager_. _SliceManager_ spawns a new _Slice_ and it hands over the connected socket and the request message to the _Slice_. Then, the _Slice_ chooses a proper _Action_ to serve the requested [OP] in cooperation with _ActionManager_. After selecting an _Action_, the _Slice_ orders the selected _Action_ to process [OP_ARGUMENT] fields. This procedure is depicted in Figure 9.  
_Slice_ can freely choose different actions depending on the type of request. The mechanism is based on the Strategy design pattern [19]. In this pattern, a main class can replace a strategy class according to situations. The adoption of the Strategy pattern provides two considerable advantages. First, _Slice_ class can provide different actions without its modification. It just chooses a proper _Action_ and delegates the _Action_ to serve an actual process. Second, there is no restriction on the argument format because the corresponding _Action_ is only responsible for understanding its argument. For example, the following MAX and REPORT requests may require different arguments, but _Slice_ is not necessary to understand the detail format. _Slice_ can handle all the requests in the same way because each corresponding _Action_ takes whole charge of handling its argument. These advantages enable third-party users to make and add customized services without affecting other parts of the system.  
[MAX] [left=5, top=10, right=50, bottom=50][temperature]  
[REPORT] [humidity]
## 3 Results and Discussion
### Results
#### 3.4.2 Throughput  
In this section, the results obtained for the network throughput are presented. The throughput represents the number of bits per second transmitted correctly, and it is a performance parameter that complements the PDR offered in the previous section. Figure 8 shows on the one hand the \"targeted\" throughput, which consists of the bits per second generated by all the applications. As can be seen, this throughput was the same regardless of whether the protocol used was HWMP or MPC-HWMP, and it was higher for the NL2 load conditions. However, the throughput correctly delivered to its corresponding destination was higher when the protocol used was MPC-HWMP. In particular, it can be  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Description** & **Value** \\ \hline
**phyLayer** & Wireless physical layer. & 802.11a \\ \hline
**controlChannelNumber** & Number of control channels. & 1 \\ \hline
**controlChannelFreeq** & Frequency of the control channel. & 5180 MHz \\ \hline
**dataChannelNumber** & Number of data channels. & 4 \\ \hline  & & 5200 MHz \\
**dataChannelFreq** & Frequency of data channels. & 5220 MHz \\  & & 5240 MHz \\  & & 5260 MHz \\ \hline
**propagationDelay** & Maximum propagation delay. & 3.333 \\ \hline  & Propagation loss model. & Log distance \\ \hline
**propagationModel** & Exponent: the exponent of the path loss & 3 \\  & propagation model. & \\ \hline  & ReferenceDistance: the distance at which the & 1 m \\  & reference loss is calculated (m). & \\ \hline  & ReferenceLoss: the reference loss at the reference & \\  & distance (dB) & 46.667 \\  & (the default is Friis at 1 m with 5.15 GHz). & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Physical layer parameters.  
checked that the throughput delivered with HWMP tended to zero when the network size was equal to or greater than 16 nodes, which is consistent with the results already commented on for the PDR.
# Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM
## 2 Related Work
### Topic Models  
In the related research of natural language processing, topic classification has always been the key research object of domestic and foreign scholars, and the LDA topic model has been widely used as a mature method. Data sparseness is an obvious feature of short texts, which has always been considered to be the reason for the low classification precision of short texts. Therefore, in response to the low feature extraction efficiency of short texts, many scholars have improved the representation ability of short texts by methods of improving algorithms or model fusion. [PERSON] [9] proposed a neural network topic model based on the framework of the Wasserstein Auto-Encoder, which is significantly better than general topic models on short text data sets. [PERSON], [PERSON] [10] combined LDA topic model with CNN with GRU and proposed an effective method to improve sentiment classification of short texts. [PERSON], [PERSON] [11] added a deep pre-training task based on enhancing the BERT pre-training task and based on the LDA topic embedding optimization results, deeply integrated with the LDA model to dynamically present the fine-grained public sentiment of events. The above scholars use the combined model to enhance the ability of feature extraction and improve classification precision. In addition, the study found that ignoring the semantic importance of some feature words may lead to low classification precision. Therefore, [PERSON], [PERSON] et al. [12] proposed a new and improved method of text representation by multiplying the \"topic-document\" matrix theta and the \"word-topic\" matrix phi, which achieved good results in the classification of Chinese news. [PERSON], [PERSON] [13] proposed a strong feature dictionary method based on LDA and Information Gain model, in which the classification precision is improved by assigning more weight to the feature items. [PERSON], [PERSON] [14] proposed an unsupervised text representation method that uses the word embedding technique Word2 Vec to obtain word vectors, which are then combined with the feature-weighted TF-IDF and the topic model LDA. This method not only improves the expressiveness of the vector space model, but also reduces the dimensionality of the document vector.  
Improvements, such as enhanced feature representation and improved comprehensiveness of feature extraction, have been made to perform feature extraction in classification studies. However, there is still the problem of inconspicuous sparse feature representation. The LDA model uses an efficient probabilistic inference algorithm to process large-scale data. Still, the comment data are short, semantically sparse, and with insufficient co-occurrence information compared to large articles. Moreover, classifying them using only the LDA model will lead to inaccurate classification results.
## 4 Trajectory Generation
### Constraint Settings  
#### 4.2.1 Motion Constraints  
As we mentioned before, the trip \(\Gamma_{i}\) between \(p_{i}\) and \(p_{i+1}\) may not be in a single domain. Considering the specific environment between each pair of spots, the mode of travel for the HAUV between \(p_{i}\) and \(p_{i+1}\) can be divided into four types. Furthermore, the velocity and acceleration should be set pre-mission according to the modes: \(underwater, aerial,transitioning(betweenwaterandair)\). Given the hodograph property of the Bezier curve--that the derivative \(F^{\prime}\) of the trajectory function \(F\) still satisfies the constraints--the motion capabilities of the HAUV involving consideration of the control points \(\{c_{1}^{\prime},c_{2}^{\prime},\ldots,c_{n}^{\prime}\}\) of \(F_{t}^{\prime}\) are denoted as:  
\[c_{i}^{(n)}=n*(c_{i+1}^{(n-1)}-c_{i}^{(n-1)}) \tag{14}\]  
As \(F\) illustrates the trajectory, velocity can be determined by \(F_{j}^{\prime}\), and acceleration can be determined by \(F_{j}^{\prime\prime}\). Therefore, for each piece of trip the motion, constraints can be generalized to:  
\[\begin{split}|v_{max}^{k}|&\geq n\cdot(c_{k+1}-c_{k })\\ |a_{max}^{k}|&\geq n\cdot(n-1)\cdot(c_{k+2}-2\cdot c _{k+1}+p_{k})\end{split} \tag{15}\]The constraints of velocity and acceleration are taken into consideration in this way.
###### Abstract  
Marine submerged buoys can effectively obtain various parameters of seawater, which plays an important role in the research of marine physical phenomena, marine environmental changes, and climate change. However, traditional self-contained submerged buoys usually work underwater at a depth of about 100 m, and the observation data cannot be obtained before their recovery, which cannot satisfy the needs of real-time data acquisition for marine scientific research. To solve this problem, this paper proposes a real-time communication subsurface mooring system that consists of a satellite communication buoy (SCB), conductivity-temperature-depth sensors (CTD), and an inductive coupling mooring cable. The underwater inductive coupling link collects the data from the underwater sensors and transmit it to the SCB. Then, the data will be transmitted to the station receiver via satellite communication module integrated into the SCB. In order to ensure a high success rate of data recovery, the stress analysis and hydrodynamic simulation of the SCB were carried out in this paper. The results show that the SCB maintained a relatively stable attitude in the 3-4 sea state. The attitude data obtained from the subsequent sea trial was consistent with the simulation results, and the success rate of satellite communication during this period was more than 95%. In this paper, a modular embedded hardware circuit was designed to meet the functional requirements of the subsurface mooring system. An efficient data recovery strategy was also developed, which ensured that the average power consumption of the system was low and the success rate of data recovery is not less than 90% when operating in the severe sea state for a long time. The system underwent sea trials in the South China Sea for more than 3 months from the end of 2021 to the beginning of 2022. It transmitted more than 2034 sets of seawater profile temperature, salinity, and depth data in real-time, with a success rate of over 91% of the total sample data. The CTD data returned in real-time from our system is consistent with the data of the HYCOM and World Ocean Atlas (WOA), and a cyclonic mesoscale eddy was detected in the operation area.  
Keywords:real-time communication; satellite communication buoy; hydrodynamic analysis; data recovery strategy +
Footnote †: journal: Marine Science and Engineering
## 5 Mechanism Description and Inverse Position Analysis
The geometric constraints of the mechanism are given as follows:  
1. Stroke of active P joints. For each type of mechanism, the strokes of the hydraulic piston rod act as the actuator, which defines the workspace. 2. Rotational angle range of the R1 joint. To avoid the interference between links, the rotational angle range of the R1 joint should be considered: \[\theta_{j}=\arccos\frac{\mathbf{q}_{j}\cdot\mathbf{s}_{1}}{\Big{|}\mathbf{q}_{j}\Big{|} \mathbf{s}_{1}},\ (j=1,2,3,4).\] (29)
3. Interferences of MP between fixed base. As for type I, due to the particularity of the fixed base configuration and limitation for range of the P joint, the mobile platform will be restricted. As expressed in Equation (30), \(\mathrm{d}_{0}\) denotes the length of F1 and F2 in the y-direction, \(\mathrm{d}_{\mathrm{mp,j}}\) denotes the distance of MP to the fixed coordinate system in the y-direction. \[\mathrm{d}_{0}\leq\mathrm{d}_{\mathrm{mp,}j},\ (j=1,2,3,4)\] (30)  
To satisfy the requirements of the actual work area \(\mathrm{x}\in(-0.17\mathrm{m},0.17\mathrm{m})\),\(y\in(-1.2\mathrm{m},-1.4\mathrm{m})\). In Figure 8, the MP workspace of type II is larger than type I, and it contains the workspace of type I. This is related to the swing length of the rotating components (leg or hydraulic), and their workspaces are approximately \(\mathrm{x}=0\) m symmetry. Figure 8: Reachable workspace of MP.
###### Abstract  
The clumping index (CI) is a key structural parameter that quantifies the non-randomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. However, accurate estimations of the seasonal CI have substantial challenges, e.g., from the need for accurate hot spot measurements, i.e., the typical feature of the bidirectional reflectance distribution function (BRDF) shape in the current CI algorithm framework. Therefore, deriving a phenologically simplified stable CI product from a high-frequency CI product (e.g., 8 days) to reduce the uncertainty of CI seasonality and simplify CI applications remains important. In this study, we applied the discrete Fourier transform and an improved dynamic threshold method to estimate the start of season (SOS) and end of season (EOS) from the CI time series and indicated that the CI exhibits significant seasonal variation characteristics that are generally consistent with the MODIS land surface phenology (LSP) product (MCD12Q2), although seasonal differences between them probably exist. Second, we divided the vegetation cycle into two phenological stages based on the MODIS LSP product, ignoring the differences mentioned above, i.e., the leaf-on season (LOS, from greenup to dormancy) and the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), and developed the phenologically simplified two-stage CI product for the years 2001-2020 using the MODIS 8-day CI product suite. Finally, we assessed the accuracy of this CI product (RMSE = 0.06, bias = 0.01) via 95 datasets from 14 field-measured sites globally. This study revealed that the CI exhibited an approximately inverse trend in terms of phenological variation compared with the NDVI. Globally, based on the phenologically simplified two-stage CI product, the CI\({}_{\text{LOS}}\) is smaller than the CI\({}_{\text{LFS}}\) across all land cover types. Compared with the LFS stage, the quality for this CI product is better in the LOS stage, where the QA is basically identified as 0 and 1, accounting for more than ~90% of the total quality flag, which is significantly higher than that in the LFS stage (~60%). This study provides relatively reliable CI datasets that capture the general trend of seasonal CI variations and simplify potential applications in modeling ecological, meteorological, and other surface processes at both global and regional scales. Therefore, this study provides both new perspectives and datasets for future research in relation to CI and other biophysical parameters, e.g., the LAI.  
**Keywords:** clumping index; seasonal variation; MODIS; spatiotemporal variation; remote sensing products; surface BRDF; hotspot reflectance