esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Arthropods and Plants Diversity
An extract from _Artemisia absinthium_ L. showed biostimulant properties in soybean cultivation [23] and long-term _C. rotundifolia_ mulching significantly influenced the soil chemical properties and bacterial communities of persimmon orchards [24]. Extracts from this plant have also shown promising biopesticide activity; however, it may affect the survival and metabolic activity of key soil organism [25]. _Centratherum punctatum_ Cass. showed pesticide activity against the mosquito _Culex quinquefasciculus_ [PERSON] (Diptera: Culicidae) [26] and extracts from _Solanum agarium_ Sendtn. showed molluscicidal activity against the snail _Biomphalaria glabata_ ([PERSON]), the intermediated host of _Schistosoma mansoni_ [PERSON] (Diplostomida: Schistosomatidae) [27]. Moreover, several of the identified plants, such as _C. erecta_, _Lourteigia ballotaefolia_ ([PERSON]) R.M.King and H.Rob., _A. absinthium_, _Centratherum punctatum_ Cass., _Sida_ spp., and _Solanum lyocarpum_ A.St.-Hil., contain compounds with medical properties or have been traditionally used as medicinal plants [28; 29; 30; 31; 32; 33; 34]. Concerning the relations among arthropods and plant species, to date, the knowledge about weed-arthropod interactions in agricultural landscapes is insufficient [35]. In this work, the RDA revealed some relations among arthropods and plants. After the forward selection, the plant species were reduced to _A. tenella_ (P1), _C. punctatum_ (P6), _L. ballioifolia_ (P2), _S. Iycocarpum_ (P7), unidentified species 2 (P20), _P. cancellata_ (P9), and _C. rotundifolia_ (P12). The RDA was significant (F = 4.969, df = 7, _p_-value = 0.001) and resulted in seven canonical axes with a cumulative contribution to the variance (explained by the explanatory variable IVIs of plant species) of 28.33%. The adjusted R2 reduced the variance explained to 22.63%. The percentages of accumulated constrained eigenvalues show that the first axis explained 22.63 \(\times\) 66.17 = 14.97% and the second 22.629 \(\times\) 27.81 = 6.29% of the variance. The first residual eigenvalue explained 20.21% of the variance, more than all RDA eigenvalues. This means that the first residual structure of the data has more variance than the structures that can be explained by the explanatory variables [36]. This could be due to a potential influence of other variables not included in the analysis. Further studies should address the ecological mechanisms that trigger these correlations and other possible factors influencing them. The triplot (Figure 3) shows that plants played an essential role in the dispersion of the sites along the first axis. The plants _A. tenella_ (P1), _C. punctatum_ (P6), _L. ballioifolia_ (P2), and unidentified species (P20) characterized sites sampled in July and August, and the arthropod 61, and to a lesser degree the arthropod 50 (both Collembola) were correlated with those plants. _S. Iycocarpum_ (P7), _P. cancellata_ (P9), and _C. rotundifolia_ (P12) characterized
## 1 Introduction  
Stabilization missions and armed conflicts entail the risk of the loss of health or life caused by personal injuries. According to the statistics published in HFM-090 [1]. Improvised Explosive Devices (IEDs) constituted the most common cause of death and personal injuries of soldiers fighting in Iraq and Afghanistan between 2001 and 2010.  
Improvised mines pose a threat to the health and life of soldiers who conduct routine tasks during patrols, transport and convoys. Light armoured vehicles are exposed to the effects of the majority of explosives which the opposite side has at its disposal. As part of preventing and combating the use of IEDs, all measures aiming at ensuring the safety of soldiers are undertaken. Companies which manufacture armoured vehicles constantly search for better materials characterised by bigger ballistic efficiency and bigger efficiency of absorbing the energy of the above-mentioned explosives in order to provide the highest level of safety for armies [2,3].  
The cervical spine, due to its location, is the most exposed and the least protected part of the entire spine. Therefore, it is the most vulnerable to injuries [4]. The mechanism of traumatological injuries is connected with ad hoc overloading of the limit values of the strength of given spinal structures [5]. At the moment of a mine explosion, a shock wave is created, and it propagates in all directions at a speed faster than the speed of sound. The shock wave creates a pulse which triggers high acceleration in a few milliseconds and causes injuries, as well as death of the crew (HFM-090). There have been many attacks where the lower extremities of soldiers are exposed to injury. Most often, IED attacks have resulted in tibia fractures, open wounds and even limb amputation [6].  
For more than ten years, two of the biggest wars in Afghanistan (2001-currently) and Iraq (2003-2011) have led to the death of around 58,000 coalition soldiers. Injuries of the musculoskeletal system were caused in 69-82% by the explosive force of Improvised Explosive Devices (IEDs) and Explosively Formed Penetrators (EFPs), whereas around 20-30% of them were caused as a result of firing or other mechanisms. The explosion of IEDs under and/or next to a vehicle, which was not unsealed but only deformed, resulted in the generation of strong acceleration. Depending on the location of an explosive, the explosion causes either the throwing of the body up and then collision of the head with the vehicle's roof or creation of injuries caused by shearing forces resulting from side/transversal acceleration [7]. The most well-known form of head protection are helmets. There are many scientific publications about head protection using protective helmets, e.g., [8; 9].
## 4 Time-Frequency Analysis for Feature Extraction
### Wavelet Scattering Transform (WST)  
The WST excels in hierarchically capturing both time and frequency details, ensuring invariance to small signal shifts while preserving crucial structures. It extends the traditional WT by cascading convolutions and employing modulus nonlinearities to extract structured information over different scales and time shifts. The process involves sequential signal analysis, where each step's output feeds into the next.
# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 3 Problem Description and Model Design
### Problem Description  
Logistics and transportation supply capacity is a key external factor in promoting the development of China's modern logistics system and its freight transportation network. The logistics industry relies on networked operations to ensure transportation efficiency, and the logistics transportation supply network has two basic characteristics, namely spatiotemporal dispersion and fragmentation of the volume, resulting in significant spatiotemporal evolution characteristics and noise in the supply data, and the network is affected by a variety of supply-related factors, which makes it difficult to forecast the supply volume.  
Logistics and transportation supply prediction is a time series prediction problem involving a set of known time series lengths of historical observations. If the length is \(L\), the time is \(t_{1}\) to the time \(t_{L}\), then the historical observations are \([x_{t_{1}},x_{t_{2}},\cdots,x_{t_{L}}]\) and the expectation of time series prediction is denoted as \(f(x_{t1},x_{t2},\cdots,x_{tL})\). To find a relationship that can satisfy the following mapping and minimize the error between the predicted value \(\big{[}\hat{y}_{t_{L+1}},\hat{y}_{t_{L+2}},\cdots,\hat{y}_{t_{L+T}}\big{]}\) and the true value \(\big{[}y_{t_{L+1}},y_{t_{L+2}},\cdots,y_{t_{L+T}}\big{]}\):  
\[f(x_{t_{1}},x_{t_{2}},\cdots,x_{tL})=\big{[}\hat{y}_{t_{L+1}},\hat{y}_{t_{L+2}},\cdots,\hat{y}_{t_{L+T}}\big{]} \tag{1}\]  
where \(L\) denotes the observation length and \(T\) is the prediction length.  
The paper focuses on the spatiotemporal evolutionary patterns of supply quantity data, designs multidimensional feature engineering to extract the feature information of related factors, and introduces the cutting-edge Informer model in the field of time series forecasting to model supply quantity. Within the Informer model, a spatiotemporal convolutional network is designed to extract data spatiotemporal features, an LSTM model is added to capture long- and short-term memory dependencies, and finally, a multilayer perceptron is used to predict future supply quantities. This creates a logistics transportation supply forecasting method based on an improved Informer model.
###### Abstract  
Land surface phenology derived from satellite data provides insights into vegetation responses to climate change. This method has overcome laborious and time-consuming manual ground observation methods. In this study, we assessed the influence of climate on phenological metrics of rubber (_Havea brasiliensis_) in South Sumatra, Indonesia, between 2010 and 2019. We modelled rubber growth through the normalised difference vegetation index (NDVI), using eight-day surface reflectance images at 250 m spatial resolution, sourced from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites. The asymmetric Gaussian (AG) smoothing function was applied on the model in TIMESAT to extract three phenological metrics for each growing season: start of season (SOS), end of season (EOS), and length of season (LOS). We then analysed the effect of rainfall and temperature, which revealed that fluctuations in SOS and EOS are highly related to disturbances such as extreme rainfall and elevated temperature. Additionally, we observed inter-annual variations of SOS and EOS associated with rubber tree age and clonal variability within plantations. The 10-year monthly climate data showed a significant downward and upward trend for rainfall and temperature data, respectively. Temperature was identified as a significant factor modulating rubber phenology, where an increase in temperature of 1 \({}^{\circ}\)C advanced SOS by -25 days and EOS by -14 days. These results demonstrate the capability of remote sensing observations to monitor the effects of climate change on rubber phenology. This information can be used to improve rubber management by helping to identify critical timing for implementation of agronomic interventions.  
land surface phenology; TIMESAT; MODIS; NDVI; time series analysis; Sumatra 1 School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD 4343, Australia; [EMAIL_ADDRESS]
## 3 Validations and Applications
### Practical Application
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Unit** & \begin{tabular}{c} **Prototype Main Deck** \\ **Section** \\ \end{tabular} & \
\begin{tabular}{c} **Scale** \\ **Ratio** \\ \end{tabular} &
\begin{tabular}{c} **Main Deck** \\ **Section Model** \\ \end{tabular} \\ \hline Width, \(B\) & m & 49.7 & 1:70 & 7.10 \\ Height, \(H\) & m & 4.4 & 1:70 & 0.057 \\ Mass per unit length, \(m\) & kg/m & 6.95 \(\times\) 10\({}^{4}\) & 1:70\({}^{2}\) & 14.184 \\ Torsion mass moment of inertia per & & & & \\ unit length, \(l\) & kg\(\bullet\)m\({}^{2}\)/m & 1.16 \(\times\) 10\({}^{7}\) & 1:70\({}^{4}\) & 0.483 \\ Vertical bending frequency, \(f_{h}\) & Hz & 0.103 & 16.83:1 & 1.7334 \\ Torsional frequency, \(f_{\mathrm{a}}\) & Hz & 0.225 & 16.83:1 & 3.7842 \\ Vertical bending–damping & & & & \\ ratio, \(\zeta_{h}\) & & 0.5 & / & 0.5 \\ Torsional damping ratio, \(\zeta_{\mathrm{a}}\) & & 0.5 & / & 0.5 \\ Wind velocity, \(U\) & m/s & / & 1:4.16 & / \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Main parameters of the main deck section of LDYB. Table 6 summarizes the flutter critical wind speed and flutter frequency results of the main girder of LDYB calculated by the aeroelastic analysis method in this study and other examples of research. It can be seen that the flutter critical wind speed of the girder of LDYB obtained by different methods is similar, while there is a slight difference between the flutter frequency. This result indicates that the aeroelastic method in this paper can simulate the flutter critical wind speed, even though the first-order Volterra model may underestimate the aerodynamic stiffness in a highly nonlinear aerodynamic system.
## 5 Future Trends and Perspectives  
The two sections above summarize the challenges and solutions for the practical applications of passive antenna sensors and systems. Some technical limitations which remain unresolved are studied in conjunction with emerging techniques to expand the applications of passive antenna sensors and systems. For this purpose, the future trends are categorized into three directions with more detailed perspectives: (1) networking and standardization: array or tag-tag coupling for improving coverage, integration with UWB technology for data-intensive applications, standardization for integration of sensing capability, evolution of Wireless Integrated Sensing Platforms (WISPs) for reduction of power consumption and integrations with more external sensors, integration with WSNs or developed into RFID sensor network (RSN), and integration with narrow-band IoT; (2) more ubiquitous and adaptable: integration with more chip-embeddable sensors, automatic impedance matching and digitalization of RSSI, analogue memory with function materials, wearable electronics for healthcare applications; (3) more simple and reliable: software defined radio (SDR) for much simple and low-cost readers, chipless antenna with variable coding mechanism, harsh environment monitoring. Based on the trends, previous publications, and long-term vision, some remarks are suggested, in particular, for the potential applications of the systems in the UK.
## 5 SCODE_plus_: Enhanced Secure Routing and Key Management Scheme  
### Hexagonal Network Deployment  
In our proposal, the target area is divided in a hexagonal grid. This model is more practical in realistic scenarios. Sensor nodes in each group are delivered together, such as using aircraft to drop groups in sequence, so expected adjacent groups have better chance of being close to each other on the ground. Based on different deployment methods, the deployment distributions follow some specific probability distribution functions (_pdf_). The _pdf_ may be a uniform distribution [27] or two-dimensional _Gaussian_ distribution [28]. In this paper, for the sake of simplify in analysis, we use a _Gaussian_ distribution, which is also widely studied and used in practice. Other distributions could be applied as well.  
We first create the hexagonal topology and determine center points of hexagons on the geographical map of the area to be deployed. Then, we map center points to the real area. Supposed a sensor networks contains \(N\) nodes. We assume that there are \(G\) cells totally. Now we split \(\left\lfloor\textit{N/G}\right\rfloor\)*_G_ nodes into \(G\) groups. Each group will be deployed within a cell with \(\left\lfloor\textit{N/G}\right\rfloor\) nodes. Each of the remainder _N_-\(\left\lfloor\textit{N/G}\right\rfloor\)*_G_ nodes is assigned to either random cells or any cells we would like to. After that, each group is one-by-one drop from the aircraft targeting to the predetermined deployment point. When the deployment point of group \(G\) is at ( \(x_{i}^{o}\), \(y_{i}^{o}\) ), the _pdf_ for a node \(n_{i}\) belongs to group \(G\) is the following:  
\[f(n_{i}(x_{i},y_{i})\left\lvert\textit{n}_{i}\in G_{i}\right\rangle=\frac{1}{2 \pi\sigma^{2}}\,e^{-\left((x_{i}-x_{i}^{o})^{2}+(y_{i}-y_{i}^{o})^{2}\right)/2 \sigma^{2}}=f(x_{i}-x_{i}^{o},y_{i}-y_{j}^{o}) \tag{1}\]  
where \((x_{i},y_{i})\) is the coordinate of node \(n_{i}\) in the group _G_\({}_{i}\) and \(\sigma\) is the standard deviation of the distribution. The value of \(\sigma\) depends mainly on the height of aircraft when dropping sensor groups.  
Based on Gaussian distribution property, 99.87% nodes reside within an area with radius of 3\(\sigma\). According to our simulation result, that ratio is 98.89%, which is almost similar. That means around 1.11% nodes resides outside the cell, it will not effect to the routing. Their positions are not far from the expected cell. Even in some case, those error nodes act as coordinators, it is still considered as the node in the expected cell, not belong to another cell.  
We define a cluster is a set of three adjacent groups. There are three types of cluster. At any group \(G_{ij}\), there are 1-cluster containing \(G_{i,j\cdot l}\) and \(G_{i\cdot l,j}\), 2-cluster containing \(G_{i,j+l}\) and \(G_{i\cdot l,j+l}\), and 3-cluster containing \(G_{i+l,j}\) and \(G_{i+l,j+l}\). For example in Figure 4, the cell [2,2] has 1-cluster cells [2,1] and [1,2], 2-cluster cells [2,3] and [1,3], and 3-cluster cells [3,2] and [3,3].
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
## 4 Experimental Results and Analysis
### Experiments on Set-Based Video Face Recognition Task  
The Honda/UCSD [26] and YouTube Celebrities (YTC) [27] datasets were used in this part of the study. The Honda dataset contains 59 video sequences, and these videos belong to 20 different categories (person). Each video contains approximately 300 to 500 frame images. In each frame, only one person exists. For fair comparison, all frames were resized to \(20\times 20\) pixels. In our following experiments, one video from each category was randomly selected for training (i.e., used as a gallery video), and the rest of the videos or image sets were selected for testing (probe videos). The YTC dataset is collected from YouTube, which consists of 1910 videos, and these videos belong to 47 different categories (celebrities). Each video contains approximately hundreds of frame images; in most cases, in each frame image, only one person is present. Similar the to Honda dataset, all frame images were resized to \(20\times 20\) pixels. For fair comparison, three image sets (video clips) were randomly selected from each person for training (i.e., used as the gallery videos), and six image sets were randomly selected for testing (i.e., used as the probe videos). Some illustrative samples obtained from these datasets are shown in Figure 3. All experiments were repeated \(10\) times, and the average experimental results are summarized in Table 2.  
This table shows the following results: First, the proposed MbLRDPL method achieved the highest classification accuracy on both the Honda and YTC datasets. Specifically, on the Honda dataset, our method achieved 100% accuracy, which was higher than that achieved by most of the other methods used for comparison. Especially compared with PML and LEML (which also use static modeling), our proposed MbLRDPL performs much better, demonstrating that our proposed LRDPL classifier has powerful classification ability. On the YTC dataset, the proposed MbLRDPL achieved the higher accuracy of 72.16%, providing lower error than other dynamic modeling methods (ISCRC, RNP, DLRC, PLRC, DRA, and JMLC) by approximately 0.96%\(\sim\)60.12%; this is a lower error than static modeling methods (CDL, PML, LEML, and MMFML) by approximately 2.93%\(\sim\)43.64%; and lower error than the dynamic plus static modeling method DML by 4.36%. Second, we observed that on the Honda and YTC datasets, our MbLRDPL was much faster than the other methods in terms of training time (Tra. Time) and testing time (Tes. Time), even compared with the hash method DML. Finally, we also observed that the dynamic modeling methods were more sensitive to the size of dataset, because when using a large dataset (i.e., YTC), their computing time was considerably longer.
# Asymmetric Propagation Delay-Aware TDMA MAC Protocol for Mobile Underwater Acoustic Sensor Networks
## 3 Simulation Results and Analysis  
We evaluate the performance of the proposed APD-TDMA protocol using simulations. To the best of author's knowledge, the BTB-TDMA is the best among TDMA MACs for MUASNs in which periodic data packet transmission is required to a SN. We compare the channel access delay (\(CD\)) and the channel utilization (\(CU\)) of the APD-TDMA with those of the BTB-TDMA. The \(CU\) is defined as the ratio of the sum of all successfully received packet durations to the sum of all cycle durations. The \(CD\) is the average interval for successfully received packets.  
Simulation environments are summarized as follows:  
1. All UNs transmit their data packets in every cycle.
2. We ignore the collisions caused by other network conditions or communication failures to clearly show the effects of asymmetric propagation delay on the network performances.
3. UNs are deployed uniformly in a three-dimensional space.
4. The velocity of UN is randomly chosen such that the speed is less than the pre-defined maximum value \(Vmax\).
5. The data rate for the acoustic modem is set to 8 kbps, and the packet length is 500 bytes, implying that the data packet duration is 0.5 s.
6. The number of cycles is set to 10,000, and 10-indepent runs are averaged. Therefore, the \(CD\) and the \(CU\) are averaged over 100,000 cycles.
7. Simulation results are drawn by varying the number of UNs \(N\), the network range \(R\), and the UN's maximum speed \(Vmax\).
# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
## 3 Related Work
### Swahili Natural Language Processing  
Efforts have been made to include Swahili among the automated languages in NLP. Most of the works are characterized by morphological analysis to resolve ambiguity. [PERSON] [41] performed a morphological analysis of Swahili using Constraint Grammar Parser while [PERSON] et al. [36] used a data-driven approach for morphological analysis of Swahili. The resultant lemma was used to compile a corpus-based dictionary. Early experiments on English-Swahili translation were presented by [PERSON] et al. [17] who used GIZA++ to carry out word alignment. [PERSON] [22] attempted to leverage the Swahili verbal mono-syllabicity morphemes with Naive Bayes algorithm for morphological  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline mbwa & mbwe & mbwi & ndwa & ndw & ndw & ngwa & ngwe & ngwi & njwa & njwe & njwi & nywa \\ nywe & shwa & shwe & shwi & chwa & chwe & chwi & pwa & pwei & pwo & swa & swe \\ swi & twa & twe & twi & zwa & zwe & zwi & cha & che & chi & cho & chu & dha \\ dhe & dhi & dho & dhu & gha & ghe & ghi & gho & ghu & kha & khe & kho & khu \\ mba & mbe & mbi & mob & mbu & nda & nde & ndi & ndo & ndu & nga & nge & ngi \\ ngo & ngu & ng’a & ng’e & ng’o & nja & nje & nji & njo & nju & nya & nye & nyi \\ nyo & nyu & sha & she & shi & sho & shu & tha & the & thi & tho & thu & vya \\ vye & vyo & bwa & bwe & bwi & gwa & gwe & gwi & jwa & jwe & jwi & kwa & kwe \\ kwi & lwa & lwe & lwi & mwa & mwe & mwi & nza & nze & nzi & nzo & nzu & ba \\ be & bi & bo & bu & da & de & di & do & du & fa & fe & fi & fo \\ fu & ga & ge & gi & go & gu & ha & he & hi & ho & hu & ju & je \\ ji & jo & ju & ka & ke & ki & ko & ku & la & le & li & lo & lu \\ ma & me & mi & mo & mu & na & ne & ni & no & nu & pa & pe & pi \\ po & pu & ra & re & ri & ro & ru & sa & se & si & so & su & ta \\ te & ti & to & tu & va & ve & vi & vo & vu & wa & we & wi & wo \\ wu & ya & ye & yi & yo & yu & vu & za & ze & zi & zo & zu & a \\ e & i & o & u & b & d & f & k & m & n & s & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Swahili syllabic alphabet.  
analysis. A breakthrough in automating Swahili occurred when the university of Helsinki developed both unannotated and annotated Swahili corpora that have been used for NLP tasks. [PERSON] [38] used the corpus to present an automatic lexical acquisition method that learns semantic properties of Swahili words by the self organizing Map algorithm. Earlier, [PERSON] et al. [42] had used data-driven taggers for part-of-speech tagging on the annotated Helsinki corpus. It should be noted that most of these works employed the inferior machine learning algorithms when compared to deep learning algorithms [1].