esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Simulation Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **X-axis** & **Y-axis** & **Z-axis** & \(\theta\) & \(\phi\) & \(\psi\) \\ \hline \(k_{P}\) & 0.69 & 0.69 & 1.32 & default & default & 0.07 \\ \(k_{I}\) & 0.00015 & 0.00015 & 0.0003 & default & default & 0.00001 \\ \(k_{D}\) & 50 & 50 & 10.2 & default & default & 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Gains of Bebop’s controllers used in experiments. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **FIB** & **GLD** & **FIB** & **GLD** & **FIB** & **GLD** \\ \hline \(\alpha\) & 1.0 & 1.0 & 0.9 & 0.9 & 0.8 & 0.8 \\ \(\beta\) & 0.0 & 0.0 & 0.1 & 0.1 & 0.2 & 0.2 \\ \(k_{Dimit}\) & 2.0 & 2.0 & 10.0 & 10.0 & 18.0 & 18.0 \\ \(k_{P}\) range & [0.5,5.0] & [0.5,5.0] & [0.5,5.0] & [0.5,5.0] & [0.5,5.0] & [0.5,5.0] \\ \(k_{D}\) range & [1.0,20.0] & [1.0,20.0] & [1.0,20.0] & [1.0,20.0] & [1.0,20.0] & [1.0,20.0] \\ No. of bootstrap cycles & 2 & 2 & 2 & 2 & 2 \\ No. of main iterations & 48 & 56 & 48 & 56 & 48 & 56 \\ Tuning time [sec] & 576 & 672 & 576 & 672 & 576 & 672 \\ Low-pass filtration & yes & yes & yes & yes & yes & yes \\ \(\epsilon\) & 0.05 & 0.05 & 0.05 & 0.05 & 0.05 & 0.05 \\ Best \(k_{P}\) and \(k_{D}\) values & 3.56/8.70 & 3.70/8.18 & 4.63/5.99 & 4.49/8.58 & 4.23/10.51 & 4.39/15.44 \\ \(J_{1}\) (after the 1 st bootstrap) & 6.8235 & 5.4575 & 5.7596 & 5.4865 & 5.9641 & 5.8197 \\ \(J_{48}\) (after 48 iter.) & 6.0969 & 5.5024 & 5.4882 & 5.4492 & 5.8815 & 5.9059 \\ \(J_{end}\) (after the tuning proc.) & 6.0969 & 5.4149 & 5.4882 & 5.4068 & 5.8815 & 6.0298 \\ \(J_{avg}\) (average for tuning proc.) & 6.7264 & 5.4754 & 5.7730 & 5.5643 & 6.0345 & 5.9376 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of simulation experiments. Figure 8: Obtained values of the \(J\) performance index for \(k_{P}\) and \(k_{D}\) combinations (left column) and \(J=f(k_{P},k_{D})\) approximations (right column) for: (**a**) \(\alpha=1.0\), \(\beta=0.0\), (**b**) \(\alpha=0.9\), \(\beta=0.1\), (**c**) \(\alpha=0.8\), \(\beta=0.2\). FIB (green) and GLD (white) tuning results for: (**a**) \(k_{Dimit}=2\), (**b**) \(k_{Dimit}=10\), (**c**) \(k_{Dimit}=18\) (marked in red).
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 3 Measurement of Fruit Size
### Prediction of Size at Harvest  
Unlike fruit number, fruits continue to increase in size in the weeks before harvest. Fruit size taken some time before harvest can be used in a forward estimate of fruit size at harvest using a growth model.  
The growth of tree fruit is characterized by three chronological stages: a cell division stage, which will set the potential size of the fruit, a cell expansion phase and a reserve accumulation phase. Across these stages, fruit size growth typically follows a double sigmoid curve. The final fruit size is influenced by the ratio of fruit (sinks) to photosynthetic capacity as determined by leaf area, photosynthetic conditions, and tree water relations.  
Growth models have been presented for a number of fruits, including apple [90; 91; 92], grape [94], kiwifruit [95], mango [96], orange [93] and tomato [98; 99] (Table 5). Growth rate (mm/day or g/day) is highly dependent on species and variety, locale (climate) and management. Under irrigated conditions, the growth curves for some fruit, such as apples and pears, can be relatively stable across localities, but growth patterns of other fruit, e.g., grapes, can vary widely between localities. Without irrigation, size growth is much more variable. For example, in a rainfed production system, grape bunch mass increase was less than 1 g/d growth (stage II) in a dry year, and 3-5 g/d after rainfall (unpublished data).  
An empirical model for fruit sizing must therefore be based on size data collected of a given cultivar and a given locality, under'standard' growing conditions of that site. In the mango fruit example of Figure 10, a linear model fitted to fruit size data between 32 and 11 days before harvest predicted a harvest fruit weight of 604 g, while the mean actual weight at harvest was 607 g.  
More sophisticated models of fruit sizing allow for variations in growing conditions. For example, [PERSON] et al. [100] reported kiwifruit growth to be most sensitive to water stress early in fruit development (6-day stress, 14 DAB), while stress in late fruit development had more impact on improved carbohydrate levels.
# Design and Analysis of Systematic Batched Network Codes
## 4 Triangular Embedding: A Structured Systematic Outer Code Design
### Triangular Embedding Design
The rows of \(\mathbf{G}_{i}\) corresponding to the \((m_{i}+1)\)th to the \((m_{i}+M_{i})\)th active packets have the form \(\left[\mathbf{I}_{M_{i}}\quad\mathbf{U}\right]\), where \(\mathbf{I}_{M_{i}}\) is the \(M_{i}\times M_{i}\) identity matrix and \(\mathbf{U}\) is the \(M_{i}\times(M-M_{i})\) uniformly random matrix. The other rows of \(\mathbf{G}_{i}\) are uniformly random. The batches generated can be transmitted following an arbitrary order. Define \(\mathbf{\tilde{G}}_{i}\) as the \(K^{\prime}\times M\) matrix by inserting zero rows into \(\mathbf{G}_{i}\) so that \([\mathbf{B}\ \mathbf{B}_{p}]\mathbf{\tilde{G}}_{i}=\mathbf{B}_{i}\mathbf{G}_{i}\). The _overall generator matrix_ of \(\text{ENC}_{n_{s}}\) can be written as \(\mathbf{\tilde{G}}=\left[\mathbf{\tilde{G}}_{1}\quad\cdots\quad\mathbf{ \tilde{G}}_{n_{s}}\right]\). According to the design of \(\text{ENC}_{n_{s}}\), \(\mathbf{\tilde{G}}\) is of the form in Figure 5.
## 4 Protocol Framework
### System and Network Model  
A two-dimensional emerging sensor network is constituted by a mass of bulky and cheap sensors which have mobility. Those mobile micro-sensors are arrayed at random in the network, and roam in cyberspace in the light of the Random Waypoint Mobility Model [32].  
Every sensor node has the ability to detect its own geographic position. Meanwhile, it can authenticate the situation information of its neighbors. Any secure node localization protocol [33] suitable for the detection of geographic location may be employed. All mobile nodes in the wireless sensor networks use loosely synchronized clocks [34] in a centralized way or in a distributed way.  
A general communication model such as bidirectional communication is adopted in the communication link between any two mobile nodes. During the life cycle of mobile sensor networks, the dead nodes whose power has run out and the damaged nodes would be excluded. The base stations in mobile sensor networks can be mobile or static, and must be safe and trusted.  
A PKI system [35] is utilized in mobile wireless sensor networks; every node is deployed with a private/public key pair [36]. Every node can obtain other nodes' public keys from the network authority. The result is that it is nearly impossible for an attacker to forge new identities for sensor nodes in the network. For the sake of detecting node clone attacks, a message freshness mechanism is required to stop replaying attacks in the protocol.  
In the system, an adversary has the capacity to catch and conquer a small percentage of legal sensors, and then takes complete control of them to obtain some secret information including private keys, credentials and cryptographic information. The adversary can operate with legal status in the network after obtaining the private information. They can mount all kinds of attacks, e.g., they can eavesdrop on packets, inject false data, and break supported protocols including sensor node localization protocols, clock synchronization protocols, and message freshness mechanisms. In addition, it is easy for replica nodes to launch denial-of-service attacks by way of deleting data packets from a benign node. Supposing the ability of the attacker to subvert legitimate sensor nodes is limited, then only a limited number of legitimate sensor nodes would be conquered. If the vast majority of legal sensor nodes are subverted, any scheme for detecting node replication attacks may then become defunct in the network. We are also working on the assumption that at least a one-hop neighbor of the clone is benign. The adversary captures and compromises benign node behind the closed doors in complete secrecy to avoid touching off automated detection for node replication attacks. At the same time, attackers can remember the subverted nodes and do not repeat to compromise the same nodes.
# Multi-Level Feature Dynamic Fusion Neural Radiance Fields for Audio-Driven Talking Head Generation
## 3 Method
### Efficient Audio-Visual Fusion Module  
During conversation, there are variations in the magnitude of facial motion in different regions. When working on tasks that involve generating audio-driven talking heads, it is essential to be aware that the impact of speech features on facial movements in different regions also varies. Many previous works [6; 7; 11; 12; 45] have implicitly learned this correlation but have not yet clearly distinguished the differences between regions at the feature level, leading to additional computational costs. Moreover, other studies [46; 47; 48] on audio-visual fusion have neglected the effort to generate audio-driven content, resulting in the need for appropriate mechanisms to fuse audio-visual information for our specific goal.  
In this subsection, we provide an efficient audio-visual fusion module that can learn potential correlations between audio features and facial spatial information. Specifically, this module aims to connect dynamic conditional features (audio features, eye movement features) with the vector representation encoded by the multi-level hash encoder of spatial point \(x\). However, due to differences in the feature scale and distribution between different modalities, low-dimensional audio features, and high-dimensional visual features are difficult to effectively fuse. Therefore, we project 3D space points onto a 2D plane; the three views not only retain the features of the original 3D space but also reduce the distribution differences between features of different modalities. Specifically, we used two layers of MLP to obtain global spatial context and combine the feature dimensions of 3D coordinate points with the audio feature dimensions, as shown in Figure 1. The module employs a set of dual-layer MLPs with non-shared parameters. It enables the extraction of a broader spectrum of features from different two-dimensional coordinate planes, which then serve as input for subsequent multi-head parallel computations.  
\[V_{f_{a},X} =MLP_{X}(P_{x}^{\prime}) \tag{11}\] \[V_{f_{a},Y} =MLP_{Y}(P_{y}^{\prime})\] (12) \[V_{f_{a},Z} =MLP_{Z}(P_{z}^{\prime}) \tag{13}\]
## 2 Materials and Methods
### Gesture Classification Algorithm  
The LSTM network was studied with two use-cases: for the'simplified' case only data about \(X\) and \(Y\) position of each target and frame were included, while the 'normal' case included data about \(X\) and \(Y\) position, as well as the measured Doppler velocity. Each gesture sample from the database was converted from a matrix into a vector of 19,200 elements for the 'normal' case (i.e., including \(X\) position, \(Y\) position and Doppler velocity) or 12,800 for the'simplified' case (i.e., without considering the Doppler effect). To overcome the varying number of detected targets for different time-slots, zero-padding was implemented to keep the constant number of 80 frames and 80 targets. Hence, the extracted gestures have a dimension of \(80\times 3\times 80\) and, \(80\times 2\times 80\), respectively.  
Long short-term memory (LSTM) network for each element in input sequence, each layer computes the following function:  
\[\begin{split} i_{t}&=\sigma(W_{ii}x_{t}+b_{ii}+W_{ hi}h_{t-1}+b_{hi})\\ f_{t}&=\sigma\Big{(}W_{if}x_{t}+b_{if}+W_{hf}h_{t-1}+b_{ hf}\Big{)}\\ g_{t}&=\tanh\Big{(}W_{ig}x_{t}+b_{ig}+W_{hg}h_{t-1}+b_{ hg}\Big{)}\\ o_{t}&=\sigma(W_{io}x_{t}+b_{io}+W_{ho}h_{t-1}+b_{ho})\\ c_{t}&=f_{i}\odot c_{t-1}+i_{t}\odot g_{t}\\ h_{t}&=o_{t}\odot\tanh(c_{t})\end{split} \tag{1}\]  
where \(h_{t}\) is the hidden state at time \(t,x_{t}\) is the input at time \(t,h_{t-1}\) is the hidden state of the layer at time \(t-1\) or the initial hidden state at time \(o\) and \(i_{t},f_{i},g_{t},o_{t}\) are the input, forget, cell, and output gates respectively. \(\sigma\) is the sigmoid function, and \(\odot\) is the Hadamard product. In a multilayer LSTM, the input \(x_{t}^{(l)}\) of the \(l\)-th layer (\(l>=2\)) is the hidden state \(h_{t}^{(l-1)}\) of the previous layer multiplied by dropout, \(\delta_{t}^{(l-1)}\) where each \(\delta_{t}^{(l-1)}\) is a Bernoulli random variable which is 0 with probability dropout [24]. Tanh activation function is defined as:  
\[\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{2}\]  
The output layer provides the probability of input data against each gesture. It uses LogSoftmax activation function to normalize output \(o_{1}\). of the network to a probability distribution over the predicted output classes.  
\[\log\text{Softmax}(o_{i})=\log\Bigg{(}\frac{\exp(o_{i})}{\sum_{j=0}\exp(o_{i} )}\Bigg{)} \tag{3}\]  
The largest value of the output indicates the classified gesture, accordingly the number of nodes of the output layer corresponds to the number of gestures being classified.
## 2 Related Work
### QoS-Oriented Papers  
Several works propose orchestration services for the resource management of edge nodes. The work described in [19] focuses on QoS, guaranteeing latency for computing tasks. It proposes a latency-guaranteed double resource auction (LGRDA) scheme to allocate resources on edge servers, matching resources to computing tasks considering multiple constraints such as bandwidth and latency. Focused on QoS, ref. [20] addresses reducing service time and increasing resource utilization. It uses a Stackelberg game algorithm for dynamic resource allocation, with a \"many-to-many\" matching algorithm between MECs and a 'one-to-many' matching algorithm between an MEC server and ESPs. The Stackelberg game can be considered a type of generalized auction.  
Ref. [21] optimizes QoS through a service placement algorithm to enhance service performance and maximize social welfare, employing a pricing mechanism for edge federation instead of a traditional auction. Ref. [15] emphasizes QoS and aims to provide low latency and high-reliability services for IoT. It employs a double auction mechanism to maximize resource utilization. In a double auction, sellers set minimum prices while buyers submit maximum bids. Matches occur when a seller's price meets a buyer's bid, making double auctions more complex and ideal for markets with many participants, such as stock exchanges.  
Ref. [22] focuses on maximizing the value of data in a federated learning framework within a collaborative cloud-edge environment, emphasizing QoS constraints like bandwidth. This study employs a reverse auction mechanism with resource allocation algorithms based on submodular value functions, using critical price theory to determine payments.
# GCN-Former: A Method for Action Recognition Using Graph Convolutional Networks and Transformer
## 3 Experiments  
### Datasets  
NTU RGB+D [31] is a large-scale human action recognition dataset that contains 56,880 skeleton action sequences. The action samples were performed by 40 volunteers and are categorized into 60 classes, including activities like drinking water, eating, brushing teeth, and dropping objects. Each sample contains a single action, with at most two subjects. The sequences are captured simultaneously from different angles by three Microsoft Kinect v2 cameras. The authors recommend two benchmarks. One is Cross-subject (X-sub), where the training data come from 20 subjects, and the testing data come from the other 20 subjects. The other is Cross-view (X-view), where the training data come from camera views 2 and 3, and the testing data come from camera view 1.  
NTU RGB+D 120 [32] is currently the largest dataset with 3D joint annotations for human action recognition. It extends the NTU RGB+D dataset, including actions like wearing headphones, shooting basketballs, and tossing ping pong balls. The dataset introduces 60 additional action classes, adding 57,367 skeleton sequences to the original dataset. In total, it includes 113,945 samples across more than 120 classes, performed by 106 volunteers and captured from three camera views. The dataset contains 32 different setups, each representing a specific location and background. The authors recommend two benchmarks. One is Cross-subject (X-sub), where the training data come from 53 subjects, and the testing data come from the other 53 subjects. The other is Cross-setup (X-setup), where the training data come from samples with even setup IDs, and the testing data come from samples with odd setup IDs.  
UT-Kinect Dataset [33] is captured using a single fixed Kinect. It consists of 200 sequences divided into 10 classes, where each skeleton has 20 joints. The dataset is recorded in three channels: RGB, depth, and skeleton joint positions. However, we only use the 3D skeleton joint coordinates.
## 3 Results and Discussion
### Climatic Conditions and Mobility  
Considering the inhomogeneous environment of the Greek territory, we examined whether the geographical location, and consequently the climatic conditions, of the burial sites had an influence on the isotopic signatures of human bone collagen. Specifically, the \({}^{18}\)O content in human tissues, which is much more prominent than that of \({}^{13}\)C, could be  
Figure 3: The mean \(\delta^{13}\)C\({}_{\text{col}}\) (v-PDB) and \(\delta^{15}\)N\({}_{\text{col}}\) (v-AIR) isotopic results of Agras compared with (**a**) Greek sites and (**b**) European sites. related to environmental factors such as temperature and precipitation. The \(\delta^{18}\)O isotopic values of human samples can be determined by measuring the \(\delta^{18}\)O of either structural carbonate (CO\({}_{3}\)\({}^{2-}\)) or phosphate (PO\({}_{4}\)\({}^{3-}\)) in the bioapatite [Ca\({}_{10}\)(PO\({}_{4}\),CO\({}_{3}\))\({}_{6}\)(OH,CO\({}_{3}\))\({}_{2}\)]. The phosphate oxygen ion is more resistant to the alterations than the structural carbonate but [PERSON] et al. and [PERSON] et al. [71, 86] demonstrated that the \(\delta^{18}\)O\({}_{\rm C}\) isotopic values of structural carbonate in human tooth enamel and bone bioapatite can be considered accurate. Considering that the major factor that affects the oxygen isotopic composition of human tissues is related to the body's water content and, thus, to the water consumed, any apparent variation in the isotopic composition of bones apatite samples is modulated by the local climatic conditions of each human's place of residence. Therefore, in order to investigate the climatic conditions in Agras from the Early Iron Age and Roman Age (Edessa) and to relate oxygen isotopes to the palaeombity studies, we present in Table 4 the \(\delta^{18}\)O\({}_{\rm{ap}}\) isotopic values of human bone apatite samples from EIA Agras and Roman Edessa. In the same table we present the \(\delta^{18}\)O\({}_{\rm{en}}\) of structural carbonate of human tooth enamel [19] from Roman Edessa, as well as the calculated \(\delta^{18}\)O\({}_{\rm{w}}\), using the equation \(\delta^{18}\)O\({}_{\rm{w}}=1.020\delta^{18}\)O\({}_{\rm{en}}-32.941\), which relates spring water in Greece to the \(\delta^{18}\)O\({}_{\rm{en}}\)(c) (v-SMOW) of tooth enamel apatite of recent humans [19]. The measured \(\delta^{18}\)O\({}_{\rm{ap}}\) (v-PDB) isotopic values (Table 4) of Agras range from \(-9.0\%\) to \(-7.8\%\) and for Roman Edessa from \(-10.2\%\) to \(-5.8\%\). The mean oxygen isotopic value of
## 2 Requirements and Challenges of the Smart Grid
### Security Issues  
According to the Electric Power Research Institute (EPRI), one of the emergent requirements facing the Smart Grid development is related to cyber security of systems [15]. The growing importance of the Smart Grid make it a sensitive target for cyber terrorists, which implies a critical concern for system designers as remarked in the EPRI report [24]. Therefore, it is mandatory that the information collected and the decisions made are sent encrypted to prevent malicious users from eavesdropping or tampering with sensitive information as many works have already outlined [25, 26].  
For small embedded devices like WSN nodes, implementing state-of-the-art encryption techniques represents an important challenge due to their hardware constraints, both in terms of computing capabilities and memory. Besides, the required extra computation leads to a non-trivial increase in the power consumption. Therefore, a thorough study of the current available encryption algorithms to select the best solution is needed. However, the National Institute for Standards and Technology (NIST) and the Federal Information Processing Standard (FIPS) has determined that the preferred solution for new deployments should be AES (Advanced Encryption Standard) to guarantee the required level of privacy in Smart Grid environments [15].
## Appendix A Example of Joint TA Selection in the SSK-LDM Model  
Assume \(N_{t}=8\) and assume that the _UL_ contributes 2 AS bits (\(b_{AS}^{L}=2\)), whereas the _LL_ contributes with 1 AS bit (\(b_{AS}^{L}=1\)). Hence, the number of groups for the _UL_ is \(G_{U}=4\) groups with \(I_{U}=2\) indices per group. Additionally, the _LL_ has \(G_{L}=2\) groups with \(I_{L}=4\) indices per group. For instance, the input AS bits at a certain transmission time are \"10\" and \"1\", respectively, from the _UL_ and _LL_ as shown in Table 11. Accordingly, \"10\" are mapped to the third group \(g(U)=3\). The second index from the third group is selected (i.e., \(i(U)=2\)) because of the data input \"1\" from the second user. Therefore, the spatial symbol \(h_{3,2}^{L}\) is selected from \(\mathcal{H}_{U}\). Similarly, \(g(L)=2\) is selected according to the data input \"1\" of the second user while \(i(L)=3\) based on the input \"10\" from the first user. Consequently, the spatial symbol \(\mathbf{h}_{2,3}^{L}\) is selected from \(\mathcal{H}_{L}\).