esa-sceva/satcom-chunk-collection huggingface dataset:
# Minimizing Maximum Feature Space Deviation for Visible-Infrared Person Re-Identification
## 4 Experiments and Analysis
### Comparison with State-of-the-Arts Methods
#### 4.3.1 Comparison on SYSU-MM01  
As shown in Table 1, we can see the experimental results on SYSU-MM01 dataset show that the proposed FSMD method obtains the great results in both all-search and indoor-search mode. Specifically, the proposed FSMD method obtains 60.64% Rank1 and 57.54% mAP in all-search mode, while obtains 62.93% Rank1 and 69.27% mAP in indoor-search mode. Compared with the feature extracting method DFLN-ViT [35], the proposed method obtains 0.80% higher Rank1 in both all-search and indoor-search mode. Compared with GAN-based method GECNet [22], the proposed method defeats GECNet [22] by 7.27% in terms of Rank1 and 5.71% in terms of mAP in all-search mode. The improvement demonstrates the superiority of the proposed FSMD method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**All-Search Mode**} & \multicolumn{2}{c}{**Indoor-Search Mode**} & \multirow{2}{*}{**Reference**} \\ \cline{2-2} \cline{4-6}  & **Rank1 (\%)** & & & & \\ \hline BDTR [3] & 17.01 & 19.66 & N/A & N/A & JCAI 2018 \\ cmGAN [6] & 26.97 & 27.80 & 31.63 & 42.19 & IJCAI 2018 \\ D-HSME [43] & 20.68 & 23.12 & N/A & N/A & AAAI 2019 \\ D\({}^{2}\)RL [27] & 28.9 & 29.2 & N/A & N/A & CVPR 2019 \\ AlignGAN [28] & 42.4 & 40.7 & 45.9 & 54.3 & ICCV 2019 \\ eBDTR [13] & 27.82 & 28.42 & N/A & N/A & ITES 2020 \\ CE\({}^{2}\)L [4] & 29.52 & 28.4 & N/A & N/A & ICPR 2020 \\ Hi-CMD [25] & 34.9 & 35.9 & N/A & N/A & CVPR 2020 \\ DGD+MSR [44] & 37.35 & 38.11 & 39.64 & 50.88 & TIP 2020 \\ JSIA-ReID [23] & 38.1 & 36.9 & 43.8 & 52.9 & AAAI 2020 \\ MSPAC-MeCen [18] & 46.62 & 47.26 & 51.63 & 61.54 & ICPR 2020 \\ X modality [20] & 49.92 & 50.73 & N/A & N/A & AAAI 2020 \\ MACE [37] & 51.64 & 50.11 & 57.35 & 64.79 & TIP 2020 \\ LAND [14] & 53.6 & 52.0 & 57.0 & 63.2 & IOT 2020 \\ DDAG [1] & 54.75 & 53.02 & 61.02 & 68.0 & ECCV 2020 \\ FBP-AL [17] & 43.78 & 42.91 & N/A & N/A & TNNLS 2021 \\ AGW [12] & 47.50 & 47.65 & 54.17 & 62.97 & TPAMI 2021 \\ LLM [30] & 55.25 & 52.96 & 59.65 & 65.46 & SPL 2021 \\ ADCNet [24] & 55.9 & 59.6 & 58.8 & 65.6 & ICME 2021 \\ DMiR [45] & 50.54 & 49.29 & 53.92 & 62.49 & TCSVT 2022 \\ GECNet [22] & 53.37 & 51.83 & 60.60 & 62.89 & TCSVT 2022 \\ DFLN-ViT [35] & 59.84 & **57.70** & 62.13 & 69.03 & TMM 2022 \\ \hline MMFSD & **60.64** & 57.54 & **62.93** & **69.27** & Ours \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance comparison on the SYSU-MM01 dataset.
## 1 _Applicational sciences_  
### _Applicational sciences_  
#### 1.1.1 _Applicational sciences_  
The current research was carried out to characterize the phytoseolicology of the forests of one of Pakistan's most valuable tree species (Deodar) across its native range. In this context, our main hypothesis was that, along the altitudinal gradient, we would find different plant communities that would be driven by different environmental variables (climatic, edaphic, and physiographic). Therefore, to assess the vegetation structure of the pure Deodar forests of the unexplored Manoor Valley (Northwestern Himalaya), Pakistan, frequent field visits were carried out during different seasons of 2015-2018. Ecological methods: Line transects sampling (23 stands) and phytoseolicological attributes were evaluated in relation to geographical and environmental variables. Various statistical software applications (i.e., PCORD, RStudio 4.0, and R 3.6.1) were used to examine all of the gathered data of plant species and environmental variables. A total of three different plant communities (_Cedrus-Isodon-Cynodon, Cedrus-Cynodon-Dryopteris_, and _Sambucus-Cedrus-Desmodium_) were identified by grouping 162 species and 23 stands in pure Deodar forests under the influence of geographic, slope, edaphic, and climatic variables, ranging from 1580.8 to 2373.8 m. The altitude (1936-2373 m), slope angle (25-85\({}^{\circ}\)), sandy (29-48%) and loamy soil texture, wind speed (1.45 ms\({}^{-1}\)), and temperature (25.8 \({}^{\circ}\)C) all had a strong influence on the _Sambucus-Cedrus-Desmodium_ community. In contrast with this, the _Cedrus-Cynodon-Dryopteris_ community showed a positively significant relationship with the northeastern slope, silty (32-58%) and sandy (15.8-55%) loamy soil texture, and barometric pressure (814.3 pa). Nonetheless, the _Cedrus-Isodon-Cynodon_ community revealed a significant positive association with the northeastern to southwestern slope, pH (6.3), wet bulb (19.7), and dew point (17.7). We found significant differences (_p_ < 0.001) among the three communities found in the pure Deodar forests in the four diversity indexes. The _Sambucus-Cedrus-Desmodium_ community has the maximum number of plants (129 species), [PERSON]'s diversity (H\({}^{\prime}\) = 3.7), and [PERSON]'s dominance (0.98) values among the recorded communities. The Pielou's evenness index value was led by the _Cedrus-Isodon-Cynodon_ community (0.97). Beta diversity showed a dissimilarity lower than 50% among the three communities. Simple term effects in the canonical correspondence analysis model revealed significant (_p_ < 0.05) differences in altitude, slope angle, slope (southeastern), and wind speed variables. The present investigation sheds light on vegetation pattern and species contribution as a function of environmental gradients and provides a baseline for future studies.  
**Keywords: plants communities; pure Deodar forests; environmental variables; multivariate approaches; Himalaya**
# Comprehensive Analysis of Maritime Cybersecurity Landscape Based on the NIST CSF v2.0
## 5 NIST CSF-Based Analysis of Maritime Cybersecurity
### Recover  
The NIST CSF's v2.0 incident recovery plan execution category covers the implementation and upkeep of recovery protocols. Ensuring the prompt restoration of assets or systems that may have been impacted or compromised by cybersecurity incidents is the main goal. In order to minimize the effects of incidents and enable a prompt return to regular operations, this entails using a methodical approach to recover infrastructure, data, and critical functionalities.  
The literature on recovery planning underscores the significance of implementing effective processes and procedures to restore systems and assets impacted by cybersecurity incidents. [PERSON] et al. (2022) conducted a notable study assessing the effects of a cyberattack on the Maritime Transportation System (MTS). Their research examined the consequences of the attack while detailing recovery actions, involving a reshaping of resource allocation and movement security plans for vehicles, personnel, and missions. Introducing the Dynamic Discretization Discovery (DDD) algorithm for the Disrupted Capacitated Continuous Time Network Design Problem (DC-CTSNDP), this study enhances existing approaches, aiming for optimal solutions with minimal losses.  
In a complementary study, [PERSON] et al. (2017) delved into an overall recovery strategy, introducing three key parameters (Detect, Isolate, and Recover) aligned with standard antivirus software practices. Their research emphasized the challenges associated with recovering lost data and explored the potential of emerging technologies for facilitating improved data analysis, particularly through the creation of action history graphs.  
When taken as a whole, these studies offer insightful information that is helpful in creating strategies and plans for effective recovery. Although [PERSON] et al.'s (2017) emphasis on overall recovery strategies and the difficulties associated with data recovery sheds light on the complex aspects of cybersecurity incident recovery, [PERSON] et al.'s (2022) optimization algorithm helps to tackle the nuances of resource allocation. When combined, these viewpoints offer a thorough outlook that can help organizations to improve their cybersecurity resilience.
# Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks +
## 3 Proposed SDN-Based System
### Reactive NDN Path Selection Strategy  
Here, we present our proposed SDN-based system and its corresponding mechanisms, aiming to the flexible and adaptive NDN operation in unstable WMN regions of smart-city backbone networks. Its main functionalities are (i) centralized monitoring of the wireless mesh network; (ii) dynamic best-path decision-making, and (iii) NDN configuration according to the selected routes. The system consists of two functional entities: (i) _the SDN_Controller_ which is the centralized control point of the network and (ii) _the Network Nodes_, which support NDN communication over WMN. A detailed description of the system components follows.
## Appendix A The Database

Table 1: [PERSON]. [PERSON] et al. [16] established a database for the rapid prediction of dam stability. A total of 50 landslide dams with complete data are included in Table A2. Meanwhile, the calculation of BI and LRBI are included in the table for comparison. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**No. Country** & **Name** & \begin{tabular}{c} **Dam** \\ **Height** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **Dam** \\ **Length** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **Dam** \\ **Width** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **Dam** \\ **Volume** \\ **(100 m)** \\ \end{tabular}  & \begin{tabular}{c} **Lake** \\ **Volume** \\ **(100 m)** \\ \end{tabular}  & \ \begin{tabular}{c} **Catchment** \\ **(km2)** \\ \end{tabular}  & **Material** & **Stability** & \begin{tabular}{c} **Material** \\ **Coefficient** \\ \end{tabular}  & **BI** & **LDSI** \\ \hline
1 & Italy & Alleghe & 16 & 550 & 1375 & 550 & 15 & 248 & \begin{tabular}{c} Debris \\ and \\ bedrock \\ \end{tabular}  & stable & 1 & 4.35 & 1.04 \\ \hline
2 & Italy & Anterselva & 45 & 960 & 1000 & 700 & 2.7 & 19.5 & \begin{tabular}{c} Rock and \\ debris \\ \end{tabular}  & stable & 1 & 5.56 & 0.78 \\ \hline
3 & Italy & Antrona & 50 & 900 & 1800 & 2000 & 6.7 & 40.8 & \begin{tabular}{c} Debris \\ and rock \\ \end{tabular}  & stable & 1 & 5.69 & 0.83 \\ \hline
4 & Italy & \begin{tabular}{c} \begin{tabular}{c} \begin{tabular}{c} \
\begin{tabular}{c}
\begin{tabular}{c} \end{tabular} \\ \end{tabular} \\ \end{tabular} \\ \hline
5 & Italy & Borta & 70 & 600 & 1150 & 2300 & 91 & 190 &
\begin{tabular}{c} Debris \\ and \\ bedrock \\ \end{tabular} & unstable & 1 & 5.08 & 1.07 \\ \hline
6 & Italy & Bracca & 4 & 530 & 350 & 40 & 0.2 & 29.6 & Debris & unstable & 1 & 4.13 & 0.70 \\ \hline
7 & Italy & Braies & 20 & 540 & 900 & 800 & 5.52483 & 29 &
\begin{tabular}{c} Rock and \\ debris \\ \end{tabular} & stable & 1 & 5.44 & 0.78 \\ \hline  
\end{tabular}  \\ \endPeng and [PERSON] [18] established a database of dam breaching cases. A total of 34 dam breaching cases with complete data worldwide are included in Table A3. Meanwhile, the calculations of \(Q_{p}\) are included in the table after the real peak outflow rate for comparison.
###### Abstract  
Two autochthonous yeast strains called F-8 and F-78 (isolated and selected from the Tikves wine-producing region) were inoculated in wine musts from Vranec and Cabernet Sauvignon grape varieties. The fermentation process and quality of the produced wines were compared to the wines produced from the same grape varieties, but with a commercial yeast strain (D-80). The fermentation process was undertaken at 23-25 \({}^{\circ}\)C for 16 days. The highest alcohol content was detected in Vranec and Cabernet Sauvignon wines fermented with autochthonous F-8 yeast strain. Conversely, Vranec wine fermented by the F-78 yeast strain contained the lowest alcohol content (14.11%). The low sugar amount (2-3 g/L) in both grape varieties is indicated the production of dry wines. The total amount of phenolic compounds was significantly higher in Vranec wines. Unlike the alcohol content, Vranec wine produced by commercial yeast strain D-80 indicated the highest concentration of total phenolic compounds (1450 mg/L) and total anthocyanins (572 mg/L), while the lowest concentrations were observed in wine fermented by autochthonous yeast strain F-78 (1612 mg/L and 470 mg/L, respectively). The maximum color intensity was measured in the Vranec wine fermented by autochthonous yeast strain F-8, while wines from Cabernet Sauvignon grape variety fermented with the same yeast strain F-8 and commercial yeast strain D-80 had comparable values in terms of color intensity.  
Saccharomyces cerevisiae; autochthonous yeast strains; commercial yeast strains; fermentation; wine quality; Vranec wines; Cabernet Sauvignon wines; total phenolic compounds; total anthocyanins; color +
Footnote †: journal: Applied Sciences
## 3 Definitions  
Given a homogeneous WSN deployed in a building for fire hazard applications with \(N\) sensors and \(M\) sinks, each sensor can adjust its maximal transmission ranges to one of the \(k\) levels: \(r_{0}\), \(r_{1}\)... \(r_{k-l}=r_{max}\) by using different transmission power levels from \(p_{0},p_{l}\), till \(p_{k-l}=p_{max}\). Initially, all sensors work in \(p_{0}\). From the application aspect, real-time and robustness are two main challenges. \(T_{max}\) is the maximum acceptable delay in reporting such a fire event to a sink node. It is required that each sensor \(i\) will report data packets to a sink node, such that:  
(1) A communication path from sensor to the sink can be found if such a path exists.  
(2) The end-to-end delay of the paths no more than \(T_{max}\).  
(3) The choice of route is adaptively changed in response to failed nodes (assumed to be caused by fire damage).  
(4) A suitable minimized power level (_min_\(\{p_{0},p_{1}\)... \(p_{k-l}\}\)) is selected to ensure transmission to satisfy (1), (2), (2) without unnecessary power dissipation.  
Each node in the network exists in one of four states (listed in the order of health degree from best to worst).  
\"_safe_\" initial state while no fire occurs.  
\"_lowsafe_\" one-hop away from an \"_infire_\" node.  
\"_Infire_\": when detects fire.  
\"_unsafe_\": detects that it cannot work correctly any longer due to a definite fire  
There is a STATE message recording current change of node state to notify its neighborhood nodes in a fire.  
STATE (INFIRE) message: If a sensor detects fire, it enters \"_infire_\" by broadcasting a message out to denote a new local fire source.  
STATE (LOWSAFE) message: The nodes in \"_safe_\" state that receive a STATE (INFIRE) message will become \"_lowsafe_\", and then broadcast a STATE (LOWSAFE) message to notify its neighbors.  
The nodes that hear the STATE (LOWSAFE) message will get to know the new state of its neighbors about fire and do nothing.  
STATE (UNSAFE) message: An \"_infire_\" node works until it cannot work correctly. Before it cannot work any longer, it enters into \"_unsafe_\" state and broadcast a message. Any nodes that detect its residual energy is too low to work will enter into \"_unsafe_\". And then broadcast a STATE (UNSAFE) message.  
Thus each sensor may change its state autonomously in response to the fire and messages it receives, as shown in Figure 1.
# Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection
## 2 Related Works
### Modelling of KQI Prediction  
While the user is experiencing an OTT service on his phone, QoE is often affected by various factors, including mainly the network, the service provider (SP) platform and the terminal. When the user is residing in a specific scenario, we can then predict the QoE of the service the user is requesting, by utilizing the network information the user is experiencing. It is a typical problem of classification or regression. Based on the prediction, one can either recommend appropriate services to the user, or find the potential service problems that the user may encounter and take prompt measures to solve them.  
In addition, the most important research in the field of web service recommendation is QoS prediction as well. Currently, collaborative filtering (CF)-based methods [3; 10; 11] are commonly used. There are two types of collaborative filtering approaches, namely model-based and neighborhood-based.  
In the neighborhood-based approach, the unknown values are predicted by employing the values of similar users or items. Model-based CF employs training data to find the user interest pattern and then predict the user's interest in the items that have not been accessed. But, model building in the model-based CF is very time-consuming.  
In [3], an integrated QoS prediction approach, which unifies the modeling of QoS data via multi-linear-algebra based concepts of tensor, has been proposed. It enables efficient web service recommendation for mobile clients via tensor decomposition and reconstruction optimization.  
A prominent problem of the above methods is that too few dimensional data are employed. For example, in [3] only three dimensions, user ID, web service and time interval, are considered for the prediction of two QoS indicators. Less information always leads to over-fitting. What's more, in most cases there is a lack of real-world QoS dataset, especially the ones containing spatial-temporal information.  
Most contemporary QoS prediction methods exploit the QoS characteristics for a few specific dimensions, and do not exploit the structural relationships among the multi-dimensional QoS data in a high-dimensional scenario.  
In this paper, the service perception dataset employed is acquired through MCS. It contains rich environmental information of the service, up to 13-dimension or higher. The prediction of KQIs with environmental features can be modelled as a typical classification problem. A promising prediction can be made by adapting appropriate machine learning algorithms.  
Furthermore, here we consider only multi-label binary classification in our study. That is, only the positive or negative labels of KQI indices are to be predicted, unlike the above-mentioned prediction of network traffic or KQI. To mitigate the interference of low-correlated attributes on the prediction, and to avoid unnecessary computational complexity, a feature selection process is performed before the classification.
## 2 Materials and Methods
### Laboratory Experiments
#### 2.3.2 Experimental Methodology  
Wind erosion tests were performed using a laboratory wind tunnel at the Institute of Mineral Resources Engineering, RWTH Aachen University, Germany (Figure 2). This facility has already been used for evaluating the effectiveness of biogenic dust suppressants [17]. Sample trays were placed centrally in the test section and exposed to a laminar airflow of 13.57 m/s for 120 s.  
In subsequent laboratory experiments, treated soil surfaces were investigated to find whether different rainfall intensities and repetitive wetting-drying cycles impact the dust control effectiveness of the investigated biomaterials. Four different rainfall intensities were tested: (a) 0.25 L/m\({}^{2}\), (b) 1 L/m\({}^{2}\), (c) 2 L/m\({}^{2}\), and (d) 4 L/m\({}^{2}\). For this, pure water was applied on the soil surfaces using a hand-held trigger sprayer. The first application of water was carried out seven days after applying biomaterials. Then, individual samples were moistened with their chosen rainfall intensities (wet-dry cycle 1) and stored in a room at ambient temperature. After seven days of drying, the specimens were tested again in the wind tunnel and then wetted once again (wet-dry cycle 2). The same procedure was used for the following three wet-dry cycles (3-5). The sample tray was weighed before and after each measurement using a PES 4200-2M (Kern & Sohn, Balingen-Frommern, Germany) with a resolution of 0.01 g. Untreated mine soil was selected as a control to calculate the dust control effectiveness for each experimental setup. Quantitative dust control effectiveness is therefore defined as the ratio of total weight loss between treated and untreated mine soil after four wet-dry cycles. Statistical analysis of measured soil losses included calculating arithmetic mean and standard deviation values.
## 4 Results and Analysis
### Debris Flow Characteristics
#### 4.1.4 Flow Resistance Coefficients  
Table 7 shows the ranges, mean values, and standard deviations of the flow resistance coefficients \(\mu\), \(\xi\), \(n\), \(C_{1}\), and \(C_{2}\) calculated using Equations (2)-(6) based on the experimental results in this study. Figure 9 shows the experimentally obtained flow resistance coefficient values according to \(\alpha\) and \(C_{V}\). The experimental results showed that \(\alpha\) had little influence on any flow resistance coefficients (Figure 9a,c,e,g,i), but \(C_{V}\) clearly affected all flow resistance coefficients except \(C_{2}\) (Figure 9b,d,f,h,j). As \(C_{V}\) increased, the values of \(\mu\) and \(n\) decreased, whereas the values of \(\xi\) and \(C_{1}\) increased. When the berm was installed, the values of \(\xi\) decreased by 4.3% when \(\alpha\) was 25\({}^{\circ}\) and \(C_{V}\) was 0.40. Except for this one observation, each flow resistance coefficient exhibited a consistent change pattern due to the installation of the berm. The installation of the berm decreased \(\mu\) by 8.6-93.8%, increased \(\xi\) by up to 563%, decreased \(n\) by 7.0-57.2%, increased \(C_{1}\) by 10.6-91.4%, and increased \(C_{2}\) by 2.9-26.4%.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Flow Resistance** & **Range** & **Mean** & **Standard Deviation** \\
**Coefficient** & & & \\ \hline \(\mu\) (Pa-s) & 0.006–0.243 & 0.089 & 0.075 \\ \(\xi\) (m\({}^{-1/2}\)-s\({}^{-1}\)) & 2240–30,764 & 9152 & 7514 \\ \(n\) (m\({}^{-1/3}\)-s) & 0.006–0.019 & 0.012 & 0.003 \\ \(C_{1}\) (m\({}^{1/2}\)-s\({}^{-1}\)) & 27.47–61.53 & 38.87 & 8.132 \\ \(C_{2}\) (m\({}^{0.78}\)-s\({}^{-1}\)) & 6.48–9.05 & 8.00 & 0.639 \\ \hline \end{tabular}
\end{table}
Table 7: Flow resistance coefficients calculated in this study.  
Figure 9: _Cont._