esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving a Process-Based Model to Simulate Forest Carbon Allocation under Varied Stand Density
## 4 Discussion
### Uncertainties of Model Simulation and Future Work  
Differences between simulated and observed results may have occurred because of the low sampling frequency of typical fieldwork. A low sampling frequency leads to low field data, which makes it impossible to carry out more comprehensive comparisons and verification. In our study, the observed stand growths that were used to test the model were from relatively young (7-11 years old) poplar plantations, resulting in uncertainties for simulating 20-year-old stands. Another source of uncertainty is attributed to the lack of soil and local meteorological data. For some sites, climate data were obtained from the nearest weather station. In future studies, more observation data, especially soil organic matter, should be collected to reduce model uncertainty.  
Regarding carbon allocation, further refinements are still required. With ongoing climate change, severe and extensive droughts are expected to occur in many regions in the future [53]. An increase in nitrogen deposition can significantly change the soil nitrogen availability. Changes in drought and nitrogen deposition directly affect the terrestrial carbon cycle and indirectly affect the allocation of assimilated carbon among plant components [7; 25]. Therefore, uncertainty over nitrogen availability may lead to uncertainties in the model performance of allocation. In addition, there have only been a few experiments on carbon allocation [45]. The model simulation cannot be corrected by experimental data and an accurate allocation mode cannot be obtained for model improvement and parameter design. More empirical studies on carbon allocation are thus needed to reduce the uncertainty surrounding the carbon allocation patterns and carbon utilization efficiency of woody plants.  
The tree DBH growth differed significantly between stands with different densities, but the relationship between stand density and height was not significant. Therefore, according to management purposes, directional cultivation could be realized through density control in order to achieve the highest yield. In the early stages, afforestation areas should be increased appropriately. When trees grow to maturity, density should be reduced to provide sufficient growth space for trees. Our study showed that increasing the planted density could increase the biomass of poplar stands and improve the ecological function related to biomass, possibly achieving increased carbon sequestration without sacrificing wood production. Therefore, it is necessary to select suitable afforestation densities or implement thinning measures according to the forest management objectives.
# Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification
## 4 Experimental Result and Discussion  
### Experimental Setup  
Our experimental setup includes an AMD Ryzen Threadripper 1950X 16-Core Processor, 128 GB of memory, and an Nvidia RTX 3090. All experiments in this paper were conducted under the same conditions, using the Pytorch 1.11.0 platform.  
In our experiments, 9 \(\times\) 9 patches serve as input to the network model. For methods involving few-shot learning to learn transferable knowledge, we randomly select 200 labeled samples from each class in the source domain dataset. To ensure fairness in the experiments, for training purposes, we select five labeled samples from each class in the target domain dataset in all comparative experiments. The remaining target domain data samples are used as test data.  
For the Few-Shot Learning (FSL) C-way K-shot task in our experiments, C is set to the number of classes in the target domain dataset, K is set to 1, and N is set to 19, meaning the support set contains 1 sample per class, and the query set contains 19 samples per class, consistent with [14].  
During training, the feature extraction network, the projection head, and the domain discriminator all use [PERSON] as the optimizer. The learning rate for the feature extractor is set at 0.001, for the projection head at 0.001, and for the domain discriminator at 0.001. The number of training episodes is 10,000.  
In MSAAN, the dimension d of the Query, Key, and Value (Q, K, V) is 8, and the number of feature group N is 8. The parameter of the adaptive feature fusion module is initialized to 1.  
In DIFSCL, all convolutional modules are initialized using Xavier initialization [40], the parameters of the batchnorm layer are set to follow a normal distribution, having an initial mean of 1 and a variance of 0.02, and biases are set to zero; the linear layers in the projection head are also initialized using Xavier parameters. The reduction ratio of channel attention is set to 16. The temperature coefficient \(\tau\) is set to 0.1 in the UP dataset, 0.5 in the IP dataset, and 0.2 in the Salinas dataset. Each method is run ten times across  
Figure 6: (**a**) False-color image, (**b**) ground-truth image, and label color of Salinas datasetthree datasets to determine the average and standard deviation for Overall Accuracy (OA), Average Accuracy (AA), and the Kappa coefficient.
## 3 VPS and CPS Based PC-HBF
### Allocation of VPSs and CPSs to Antennas
In this algorithm, we initially consider a VPS-only PC-HBF, which comprises \(M\times U\) VPSs and no CPSs. In each iteration, a VPS is replaced by a CPS in a greedy manner. Specifically, in step 1 of Algorithm 1, \(\hat{\mathbf{F}}_{VC}\) is initially set to \(\hat{\mathbf{F}}_{V}\), which can be obtained based on the phase of channel coefficients, as described in Section 2.1. In step 1, \(\mathcal{A}\) represents the index set of the phase shifters that have been converted from VPS to CPS. In an iteration, among the unassigned CPSs in the \(u\)th subarray, whose indices are stored in \(L_{u}\), the CPS with the smallest phase difference relative to the \(m\)th VPS replaces the \(m\)th VPS to generate a new RF precoder \(\mathbf{F}_{VC}\), as described in steps 7 and 8. Then, in step 9, we compute the corresponding sum-rate \(C_{m,u}\) by using the capacity formula in (3). In step 10, the index of the selected CPS is stored as \(D_{m,u}\). We repeat this operation for all the remaining VPSs across subarrays. After searching all the remaining VPSs, in step 15, the best VPS-to-CPS conversion that yields the maximum sum rate is selected. In steps 16-18, the best RF precoder \(\mathbf{F}_{VC}\), set of unassigned CPSs \(L_{u}\), and set of selected VPSs \(\mathcal{A}\) are updated. Finally, by usingthe RF precoder \(\hat{\mathbf{F}}_{VC}\), the digital precoder \(\hat{\mathbf{V}}_{BB}\) is computed, which is subsequently normalized by the power-scaling factor \(\gamma\) in step 22. ```
1:H,F\({}_{V}\),f\({}_{\mathbb{C}}\). 2:\(\hat{\mathbf{F}}_{VC}\),F\({}_{BB}\). 3:Initialize \(\hat{\mathbf{F}}_{VC}=\mathbf{F}_{V}\), \(L_{1}=L_{2}=...=L_{u}=\{1,...,N_{\mathbb{C}}\}\), \(\mathcal{A}=\) an empty set. 4:for\(j=1\)to\(N_{\mathbb{C}}\times U\)do
5:for\(u=1\)to\(U\)do
6:for\(m=1\)to\(M\)do
7:if\((m,u)\
otin\mathcal{A}\)then
8:\(\mathbf{F}_{VC}=\hat{\mathbf{F}}_{VC}\)
9:\(l^{\star}\)= arg \(\underset{l\in L_{\mathbb{C}}}{\min}\{\angle[\mathbf{h}_{u,u}]_{m}-\angle[ \mathbf{f}_{\mathbb{C}}]_{l}\}\)
10:\([\mathbf{f}_{VC,\mathcal{A}}]_{m}=[\mathbf{f}_{\mathbb{C}}]_{l^{\star}}\)
11: Compute \(C_{m,u}\) based on (3). 12:\(D_{m,u}=l^{\star}\)
13:endif
14:endfor
15:endfor
16:\((m^{\star},u^{\star})=\arg\max_{(m,u)\
otin\mathcal{A}}C_{m,u}\)
17:\(l^{\star}=D_{m^{\star},u^{\star}}\)
18: Update \([\hat{\mathbf{f}}_{VC,\mathcal{A}}]_{m}=[\mathbf{f}_{\mathbb{C}}]_{l^{\star}}\). 19: Remove \(l^{\star}\) from \(L_{u}\). 20: Add \((m^{\star},u^{\star})\) to \(\mathcal{A}\). 21:endfor
22:Compute \(\hat{\mathbf{V}}_{BB}\) based on (4). 23:Compute \(\gamma=\frac{\gamma_{T}\{\hat{\mathbf{F}}_{VC}\hat{\mathbf{V}}_{BB}\mathbf{V} _{BB}^{H}\mathbf{F}_{VC}^{H}\}}{P}\). 24:\(\hat{\mathbf{F}}_{BB}\leftarrow\frac{1}{\sqrt{\gamma}}\hat{\mathbf{V}}_{BB}\). ```  
**Algorithm 1** Greedy allocation of VPSs and CPSs.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 6 Attacks that Require Advanced Detection
### Community Manipulation  
Community manipulation, by altering BGP community attributes to influence routing decisions, might benefit from early detection through data from multiple vantage points and group-level AS detection. This manipulation is often subtle and can have widespread effects on routing, making it difficult to detect without comprehensive visibility across different parts of the Internet. Early detection through diverse observations can identify unusual patterns of community attribute usage that deviate from normal behavior.  
Community manipulation can involve complex interactions across ASs, as community attributes are used to control routing policies between ASs. Detecting this type of manipulation requires an understanding of how different ASs interpret and act on community values, which can vary widely. Techniques that can analyze these complex interactions are crucial for identifying and mitigating the effects of community manipulation.  
This type of attack involves a sophisticated understanding of how BGP community attributes are used within the global routing system to influence routing decisions. Manipulating these attributes to achieve a malicious outcome requires an in-depth knowledge of BGP operations and the policies of various ASs, fitting the criterion for sophisticated manipulation.  
Community manipulation is inherently stealthy, as it involves tweaking specific attributes that influence routing decisions without directly altering route paths or AS paths. This subtlety makes it a prime candidate for detection techniques that can aggregate and analyze data from multiple sources, looking for inconsistencies or anomalies that could indicate manipulation.
# Topic Word Embedding-Based Methods for Automatically Extracting Main Aspects from Product Reviews
## 2 Background
### Motivation
#### 2.1.1 Automatic Prior Knowledge Estimation  
Since topic models use the Bayesian probability model, the prior probabilities \(\alpha\) and \(\beta\) should be given as the input parameter. Here, \(\alpha\) is the distribution of topics in a document and \(\beta\) is the distribution of words in a topic.  
Previously, the default values have been widely used as the priors or arbitrarily determined by domain experts. However, such approaches have the following disadvantages.  
* First, the more accurate the priors for a given data is, the faster the convergence is. However, we cannot expect this benefit if we use the existing methods.
* Second, it is hard to manually find the exact priors from a large-sized data set.
* Third, the topic model with inaccurate priors is likely to extract dirty topics, which prevent us from understanding the nature of the given data set.  
As such, it is significant to grasp proper priors in advance given a data set, in order to obtain good topic results. According to a recent study in Reference [9], there exists prior knowledge suitable for a domain and the accuracy of topic models will be greatly improved if the domain-optimized prior knowledge is used in the initial step.  
To optimize \(\alpha\) and \(\beta\), some traditional topic models use non-parametric hierarchical prior distribution. The non-parametric hLDA is based on the Chinese Process to detect the number of clusters. However, as the main disadvantage, it is considerably affected by larger clusters. In addition, note that text data are usually ambiguous and noise big data so that the mathematically-based model cannot be applied simply. In practice, to handle such noise data, the most realistic approach is to use an empirical method that automatically determine the priors by considering all combinations of \(\alpha\) and \(\beta\) values, which is not covered in the existing studies.
# AVSS: Airborne Video Surveillance System
## 3 Design of the Proposed System
### Fleet Platform  
Fleet platform provides network and acts as a translator between the server and the acting UAVs. The server can also be called ground control station (GCS), because it is stationed on the ground for the operator. Fleet platform has some portion implemented in the UAV and some in the server, but it is separately described because each of the network and translation functionalities need to be explained together. The general description of the fleet structure is provided in our previous work [15], and the modifications made are as follows.  
As shown in Figure 1, fleet platform works as a middleware between the surveillance server, i.e., GCS, and the surveillance UAV. An ad-hoc network supports the fleet platform because multi-hop capability was required for the scalability and wider a mission range. The routing protocol we use is a centralized distance and network status-based GCS-routing, which is elaborated in our previouswork [15; 16]. Yet, network security is critically impaired with ad-hoc network because most commodity hardware is not equipped with a hardware module for WPA/WPA2 encryption, which drops the throughput down to 10%. Thus, we use WEP security although it is less safe as a trade-off. The discovery of UAVs is carried out using UDP broadcasting. This is an important feature as UAV network can always be attacked and down for an arbitrary period of time, which then requires automatic discovery to repair the network. Traffic flow control is mainly for the multimedia stream as commands and flight information are not heavy enough to create a traffic flow. The server works as a DHCP server and assigns an IP address to each UAV in the fleet.  
The coordination and translation of messages are also made in the fleet, so that the drone can operate under the command of the server/operator. First, an operation that has high chance of connection failure is rejected. GCS-routing protocol predicts which topology may be broken; hence the connectivity is thus ensured for deployments. As a technical detail, each UAV has its own coordinates, which is called _local positions_. Server, on the other hand, holds _global positions_ as its coordinates, because it needs to manage all UAVs. Therefore, the location commands such as movement or hovering need translation from the global position and local positions. The rate of local position and the attitude information update is 250 Hz, and the global position information is updated 5 times each second. When the connection is broken, the UAV tries to connect back to the server for 10 to 20 s while hovering in the same location, and moves back to base. If it reconnects, it stops going back and waits for the command. When the connection fails, GCS waits for reconnection.
# Utilizing Soft Computing Techniques to Estimate the Axial Permanent Deformation of Asphalt Concrete
## 3 Materials and Testing
### Aggregate  
Crushed quartz sourced from the Amanat Baghdad asphalt concrete mix plant in Taiji, north of Baghdad, was used to prepare the asphalt mixtures in this work. The coarse and fine aggregates were sieved and blended in appropriate proportions to meet the gradation requirements for surface, binder, and base courses, following ASTM D 3515. Five different gradations were evaluated: three for the surface course with varying aggregate fractions passing the 12.5 mm and 4.75 mm sieves, identified as WCG 1, WCG 2, and WCG 3, with WCG 3 having a higher proportion of coarse aggregate (retained on the 4.75 mm sieve) compared to the other two types of surface course mixes; one for the binder course (BiCG); and one for the base course (BaCG). The gradation of the used aggregate is shown in Figure 1. Moreover, the physical properties of the aggregate are presented in Table 2 and compared with the SCRB recommendations [19].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tested Properties** & **Results** & **ASTM Standard** & **SCRB Requirements** \\ \hline
**Fine aggregates** & & & \\ Apparent specific gravity & 2.695 & C-127 & - \\ Bulk specific gravity & 2.661 & C-127 & - \\ Sand equivalent (\%) & 52 & D-2419 & 45 minimum \\ Water absorption (\%) & 0.901 & C-127 & - \\ \hline
**Coarse aggregates** & & & \\ Apparent specific gravity & 2.691 & C-127 & - \\ Bulk specific gravity & 2.631 & C-127 & - \\ Los Angeles abrasion (\%) & 19.3 & C-127 & 30 maximum \\ Fractured pieces (\%) & 94 & C-88 & 95 minimum \\ Water absorption (\%) & 0.458 & C-127 & - \\ Soundness loss by sodium sulfate solution (\%) & 3.9 & C-131 & 10 maximum \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical properties of the used aggregates.  
Figure 1: The gradation of aggregates used in asphalt concrete mixtures.
# RF Exposure Assessment for Various Poses of Patient Assistant in Open MRI Environment
###### Abstract  
In this study, the radio-frequency (RF) energy exposure of patient assistants was assessed for an open magnetic resonance imaging (MRI) system based on numerical computations of the head and body RF coil. Various poses of the patient assistants were defined to see how poorly they affected the RF energy exposure. For the assessments, the peak spatial-averaged specific absorption rate (SAR) levels were carefully compared with each patient assistant pose based on the finite-difference time domain calculations of RF coil models when the patient was placed in such coils in a 0.3 Tesla open MRI system. Overall, the SAR levels of the patient assistant were much lower than those of the patient. However, significantly increased SAR levels were observed under specific conditions, including a larger loop size of the patient assistants' arms and a closer distance to the RF coils. A comparably high level of SAR to the patient's body was also found. More careful investigations are needed to prevent the increase of SAR in patient assistants for open MRI systems at higher field strengths.  
open MRI system; dosimetry; RF coil model; patient assistant exposure; SAR; safety +
Footnote †: journal: Applied Sciences  
1
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 3 Classification of Attacks in VANETs
### Classification Based on Security Services  
* _Attacks on confidentiality:_ When the information is exchanged between vehicles, various solutions like public keys and certificates are used to encrypt the information and make it confidential. Still, attackers launch various kinds of attacks on the confidentiality of information using novel attack methods. Some common and popular attacks launched on the confidentiality of information include man-in-the-middle attack, traffic analysis attack, social attack, and eavesdropping attack.
* _Attacks on data integrity:_ With the help of integrity, it is made sure that the information transferred is not modified, delayed, or deleted during the transmission process. Attacks that may be launched against this security service include masquerading attack, replay attack, message tampering attack, and illusion attack.
* _Attacks on availability:_ Availability defines that all the information should be available to legitimate users when they require it. If data is not available to the right person at the right time, then it means vehicular networks are not working efficiently. Attacks that may be launched against this security service include DoS/DDoS, sleep deprivation, jamming attacks, jellyfish attack, intelligent cheater attack, blackhole attack, greyhole attack, greedy behavior attack, spamming attack, etc.  
_Attacks on authentication_: Authentication is also a significant service that ensures that the information is provided to users after proper cross-checking. This way, with the help of authentication, the share of information that belongs to a specific user is provided to that particular user only. However, still many types of attacks may be launched against authentications include sybil attack, tunneling attack, GPS spoofing, free-riding attack, certificate/key replication attack, etc.
* _Attacks on non-repudiation_: With the help of this service, it ensured that once after sending any particular message, the sender cannot say that he has not sent any message. In case of any dispute, this service provides proof regarding the message sent by the attacker. Repudiation attacks and loss of event are attacks in this category. Some of the attacks in this category are shown in Table 1.
## 1 Introduction  
Modeling and characterizing forest stands on small scales using high-resolution remote sensing data requires spatially explicit linking of inventory information and remote sensing data [1; 2; 3]. The exact sampling positions of field inventory plots are often determined using non-differential [4] or differential Global Navigation Satellite System (GNSS), which gives rise to location errors of up to several meters. In comparison, airborne laser scanning (ALS) provides absolute horizontal accuracies of about 25 cm (at 400 m flight altitude above ground) [5].  
The effect of positional displacements between terrestrial reference data and ALS data has been investigated by [PERSON] and [PERSON] [(6)] and [PERSON] et al. [(7)] at the sample plot level. They found that with increasing positional displacements the performance of biophysical models decreases in relation to the variable and stand characteristics. This problem exacerbates on individual tree level, since survey trees might get incorrectly linked to aerial detected trees (e.g., using ALS), which results in erroneous output data for further analyses.  
Individual tree information has a specific added value for quantitative modeling compared to plot-based methods insofar as it includes features which address structural tree characteristics [(8; 9)]. A general trend towards the usage of ALS data is recognizable [(2)]. Densities of about five to ten pulses per m\({}^{2}\) are sufficient for tree detection [(10; 11)]. It has been shown that most methods perform well in the upper canopy layer, but the identification of understory trees is challenging, because the laser beam is not able to completely penetrate each canopy layer [(3; 11)]. Tall or isolated trees can be detected most reliably with detection rates between 55% and 100%, while detection rates for trees in a group of similar trees decline to 30% to 80% [(11)]. Usually, only approximately 40% of the trees next to or below taller protruding trees are detectable [(11)]. Additionally, varying forest conditions result in a bias in the detection of large trees [(12)].
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 4 Experimental Results
### Predictor Settings
#### 4.2.4 Register Size  
The results shown in Figure 9d suggest that the value of the register size, \(R\), has almost no impact on the compression effectiveness, which means that the overflow may be so rare that there are few occasions when a large value of \(R\) is needed to prevent overflow. The compression will still be lossless, even when an overflow occurs. Consequently, a large value of \(R\) is not required because it is unlikely to improve compression and will also increase the implementation complexity. Therefore, the value of \(R\) can be set to the minimum value allowed by the standard, which is 32.
## 3 Results
### Main Findings of the Included Studies
In this case, report [49], a 46-year-old man, successfully discharged after ten days of hospital admission due to severe COVID-19, performed a pulmonary telerehabilitation program; at the beginning, his CIS fatigue scale score of 43 exceeded the threshold for \"severe fatigue\". Specifically, the rehabilitation program, designed by [PERSON], consisted of a qualified physiotherapist's supervision via WhatsApp text messaging, video, and audio calls for 3 weeks. At the end, the CIS fatigue scale score was 11, indicating normal fatigue.