esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Purpose of Study  
This paper shares the following purposes to solve the dynamic encryption key application problem in the low-power wireless communication environment mentioned in Section 1.2.2. This paper proposes a method to use communication at least once based on the asynchronous communication method when generating a dynamic encryption key between devices, generating the same encryption key based on the asynchronous method even when packet loss occurs. The dynamic encryption key generated using the proposed method is unpredictable, and even if the current key is stolen, the new subsequent key cannot be guessed, thereby satisfying the security requirement. This paper proposes a dynamic encryption key method that can be used on low-performance devices.  
The approach of this paper applies the mechanism of blockchain to the dynamic encryption key generation. The study by [PERSON] et al. [23] generated unpredictable hash values dynamically based on the IoT sensor data; using this, proposed a consensus algorithm that selects block generating nodes. The study by [PERSON] [24] generated unpredictable hash values dynamically based on the transaction history generated by microcontroller units (MCUs) in controller area network (CAN) protocol environment for security; using this, proposed a method of generating a new dynamic encryption key. The mechanism of blockchain applied in the related studies [23; 24] employs a method of dynamically generating unpredictable hash by transaction history. The newly generated hash value can be known if and only if all the details are known from the not tampered first transaction to the current one. In this paper, we also employ such mechanism of blockchain [25; 26; 27] to generate block data records based on the sensor data between devices. The difference from the related work [23; 24] is that a new dynamic encryption key is generated through a dynamically generated hash value based on the sensor history between IoT devices. Furthermore, we propose a method to generate a same encryption key by asynchronous communication method using the TargetValue and the FrequencyTable based on the sensor data history.
# Shoulder Kinematics Assessment towards Exoskeleton Development
## 2 Biomechanical Models of the Shoulder and Its Complexity  
The shoulder also known as glenohumeral joint that articulates between the rounded head of the humerus and the shallow peer-shaped glenoid cavity of the scapula. It is one of the most complex human joints, granting its largest range of motion. As shown in Figure 1, it is composed of mainly three bones named Clavicle, Scapula, and humerus [8; 9].The clavicle is a thin s-shaped bone, it is the only connection between the shoulder joint and the body trunk, also known as sternum, at one of its ends forming the Sternoclavicular (ST) joint. On the other end, it links through the Acromioclavicular (AC) joint with the Acromion, a process of the Scapula or, as it is known due to its shape, the shoulder blade. The Humerus, the largest bone of the shoulder, is coupled with the Glenoid Fossa that is contained in the shoulder blade and the humerus head, forming the Glenohumeral (GH) Joint. This union resembles a ball-and-socket joint and 2/3 of the total ROM of the shoulder is attributed to it. The wide ROM is due to the humerus head being to big for the cavity of the glenoid fossa; however, it makes this joint the most unstable of all.  
Mathematically modeling the shoulder mechanism is not an easy task, apart from the bony structure, ligaments that attach each bone and muscles are involved in the dynamics and kinematics of it. Consequently, the shoulder bones intercepts each other at different locations, making it a multiple closed-loop chain mechanisms, thus, increasing its complexity. one of the earliest mathematical models of the shoulder complex consisted of 21 muscles and a three rigid body twelve DOF mechanism [10], 3 DOF orientation for each bone, and 3 DOF the displacement of the Humerus head. Another model was presented by [PERSON] [11; 12], obtaining good results. The model contains four bones, three joints, three extracapsular ligaments, the scapulthoracic gliding plane, and 20 muscles and muscles parts, resulting in a seven DOF mechanism, these models can be used to study the morphological structure of the shoulder. Additionally, mathematical models are used to produce an estimated shoulder workspace that can be later used for exoskeleton design [13; 14; 15; 16]. More novel and simplistic models for the kinematics are studied in [6], where IMU sensor data is collected for upper limb kinematic reconstruction, modeling the shoulder as a five DOF mechanism. Furthermore, some researchers neglect the effect of the AC and ST joints, and represent the shoulder kinematics as a pure three-DOF spherical joint [17], assuming the center of rotation of the GH joints is accurately known and fixed.
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 3 Methods
### Loss Functions  
The conventional loss function we used is based on the positional loss at each step of the iteration. This is the obvious choice as the model is fully differentiable and so encoding the direct objective into the loss function helps the model perform best. However, due to the nature of the LM optimization algorithm, a loss function that minimizes the difference was considered as well.  
As the entire network was end-to-end differentiable, we could directly backpropagate the positional loss to the feature encoders. Furthermore, we could add the intermediate position after each iteration to stabilize the training process. We denoted the following positional losses:  
\[\mathcal{L}_{pos}=\sum_{i}\sum_{l}(|\hat{X}_{i}^{l}-X^{*}|+|\hat{Y}_{i}^{l}-Y^ {*}|+|\hat{R}_{i}^{l}-R^{*}|), \tag{10}\]  
where \(\hat{X}_{i^{\prime}}^{l}\), \(\hat{Y}_{i}^{l}\) and \(R_{i}^{l}\) denote the predicted longitude, latitude, and angle at iteration \(i\) and level \(l\), while \(X^{*}\), \(Y^{*}\) and \(R^{*}\) denote the GT pose.  
As another type of loss function, we can calculate the L2 difference between the projection of the deep features at the ground-truth position and the deep features of the ground-level image by normalizing the features and calculating the sum of the element-wise square of the differences:  
\[\mathcal{L}_{L2}=|(norm(proj(T))-norm(G))^{2}|_{1} \tag{11}\]  
It is important to note that the model could optimize this loss by mapping all pixels to the same deep feature, resulting in a nondescript feature space. As such, instead of relying on this loss function, we only used it as a regularization term during the first epoch as a warm-up term. We decreased the importance linearly towards 0 during the first epoch, and afterward, we did not use this loss function anymore.
## 2 Methods
### Computational Details  
Ab initio modeling was performed at the density functional theory (DFT) level in Vienna Ab initio simulation package (VASP) [32; 33]. To include van der Waals interactions of guest molecules within the MOF, the exchange-correlation functional vdW-DF was used [34; 35; 36; 37]. The energy cutoff was set to 600 eV and only the \(\Gamma\) point was considered due to the size of the system. The 54 atom rhombohedral primitive cell of Zn-MOF-74 was first relaxed until the forces on the atoms were below 1 meV/A. Binding energies were then calculated for MeOH, H\({}_{2}\)O, OH, and H (and deuterated equivalents), with the hydrogen or deuterium at the metal center and on the oxygen atom of the MOF organic linker. After the guest molecules were placed into the MOF, each structure was again relaxed until the forces on all atoms were below 1 meV/A. Reaction barriers were calculated with a standard transition-state search algorithm, i.e., the nudged-elastic band method [38; 39], as implemented in VASP.
## 2 System Model of the TR Acoustic Communication Using FMT  
### Transmit Structure  
The block diagram for the transmit structure based on baseband model is displayed in Figure 1. The information symbols \(a\left(nT_{b}\right)\) with the symbol interval \(T_{b}\) are firstly processed by a serial-to-parallel converter. Each output signal \(a_{i}\left(nT\right),i=1,\ldots,M\), of the serial-to-parallel converter is then processed by a \(K\)-times up-sampler, transmit filter and carrier modulator. Through combining all modulated signals, the transmit FMT signal can be obtained finally.  
Referring to Figure 1, the transmit FMT signal can be expressed in complex baseband as:  
\[x\left(kT_{c}\right)=\sum_{i=1}^{M}\sum_{n=-\infty}^{+\infty}a_{i}\left(nKT_{c }\right)g\left(kT_{c}-nKT_{c}\right)e^{\frac{j2\pi}{M}k} \tag{1}\]  
where \(T_{c}=T/K\) denotes the symbol interval after up-sampling and \(g\left(kT_{c}\right)\) denotes the time domain response of the transmit filter \(G\left(\omega\right)\). To obtain the highest spectrum efficiency, the transmit filter should be an ideal low-pass filter. However, since the ideal low-pass filter cannot be achieved, in fact, the transmit filter must be designed to strike a balance between the implementation complexity and the spectral containment [28, 33]. In the proposed method, we follow the transmit filter design scheme proposed by [PERSON] _et al._[28] which selects the root raised cosine (RRC) shaping filter for transmit filtering. Figure 2 shows the complex baseband spectrum of the transmit FMT signal \(x\big{(}kT_{c}\big{)}\) when the RRC shaping filter is used. Referring to Figure 2, the entire band \(B\) is divide into \(M\) subcarriers, the occupying bandwidth of each subcarrier is \(B\big{/}M\), the effective bandwidth of each subcarrier is \(B\big{/}K\), and the roll-off factor of the RRC shaping filter is \(\alpha=K\big{/}M-1\). Since \(\alpha\) must be in the range of \(\big{[}0,1\big{]}\), \(K\) can change from \(M\) to \(2M\) for a given \(M\) to compromise between ICI/ISI sensitivity and the symbol rate. Figure 2 indicates that since the maximum side-lobe peak of the transmit filter in the FMT modulator is designed very close to zero in practice, the effect of ICI can be neglected in the stable acoustic channel. Furthermore, since the subcarriers in FMT have relatively wide bandwidth, ICI also is a minor concern compared with ISI in the acoustic channel with realistic Doppler shift.  
Figure 1: Block diagram for the transmit structure of the proposed method.  
Figure 2: The complex baseband spectrum of the transmit FMT signal.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 1 Introduction  
Wireless sensor networks (WSNs) are a new type of network that integrates the various technologies of sensors, computers, communications, and other multidisciplinary information acquisition and processing. With the rapid development of Internet of Things (IoT) technology, WSNs have been widely used in environmental monitoring, smart homes, medical health and other fields, and have a wide range of application prospects in industrial and military fields. According to whether the nodes in the network have the same function, WSNs can be divided into homogeneous WSNs and heterogeneous WSNs (HWSNs). The nodes of homogeneous WSNs have the same type and function, making them suitable for single data collection and processing. HWSNs can be composed of a variety of different types and functions of sensors, such as temperature and humidity sensors, combustible gas sensors, infrared sensor modules, and flame sensor modules to adapt to diverse data needs and environmental changes. Current research on WSNs mainly focuses on the field of homogeneous WSNs. Unlike homogeneous WSNs, sensor nodes in HWSNs have different resource configurations, such as computing power, communication capacity, storage capacity, and energy supply. This diversity can better meet the needs of practicalapplication scenarios. At the same time, due to the limited energy sources for sensor nodes, extending their lifecycle while ensuring reliable transmission has become a key issue. Therefore, designing efficient routing protocols is an important topic for studying energy efficiency.
## 5 Two-Way Measurement Method in CCFD
### Principle of Two-Way Measurement Method in CCFD  
The two-way measurement method in CCFD is described as follows: (1) In terms of the time slot schedule uploaded from the ground, the satellite pair is identified to establish the link. (2) The two satellites (satellite \(i\) and \(j\)) agree on the same transmission time and simultaneously send signals to each other. The signals share the same frame structure, with the baseband clock and carrier frequency generated locally by each satellite. (3) While transmitting, each satellite synchronously receives the signal sent by the other satellite. (4) Through spatial-domain, RF-domain, and digital-domain self-interference suppression, the self-interference signal can be reduced to the receiver noise floor, enabling effective signal reception. (5) The satellites execute signal acquisition, tracking, and demodulation processes to recover the information frame, extract the transmission epoch time, and integrate it with the local epoch time to compute the local pseudo-range. (6) The local pseudo-range is embedded into the information frame and transmitted to the other satellite. Both satellites then construct the two-way observation equation using the local pseudo-range and the pseudo-range demodulated from the received information frame. (7) The system performs timestamp calculation and key error correction to derive the modified pseudo-range equation. (8) The system performs summation and difference operations on the modified pseudo-range equation to derive the inter-satellite geometric distance and clock bias, which are necessary for precise orbit determination and time synchronization.  
Figure 6 illustrates the timing diagram of inter-satellite two-way measurement in CCFD, with the following parameter definitions: (1) \(t_{i}^{s}\) is the local transmission time of the satellite \(i\), and \(t^{s1}\) is the corresponding BeiDou time (BDT). \(t_{j}^{s}\) is the local transmission time of the satellite \(j\), and \(t^{s2}\) is the corresponding BDT. (2) \(t_{i}^{r}\) is the local reception time of the satellite \(i\), and \(t^{r2}\) is the corresponding BDT. \(t_{j}^{r}\) is the local transmission time of the satellite \(j\), and \(t^{r1}\) is the corresponding BDT. (3) \(\tau_{i}^{ts}\) and \(\tau_{i}^{tr}\) represent the transmission and reception channel delays of the satellite \(i\), respectively. \(\tau_{j}^{ts}\) and \(\tau_{i}^{tr}\) represent the satellite's transmission and reception channel delays \(j\), respectively. (4) \(\tau_{i}(t^{s1})\) and \(\tau_{i}(t^{r2})\) denote the position vectors of the satellite \(i\) in the Earth-Centered Inertial (ECI) coordinate system at the transmission and reception times, respectively. \(\mathbf{r}_{j}(t^{s2})\) and \(\mathbf{r}_{j}(t^{r1})\) denote the position vectors of the satellite \(j\) in the ECI coordinate system at the transmission and reception times, respectively.
## 4 Proposed Method: eDA3-X  
### Neural Network Architecture  
The key idea of proposed method, eDA3-X, is to consistently use the _saliency vector_ (trainable parameters) throughout eDA3-X, as illustrated in Figure 2. In this approach, eDA3-X is capable of visualizing relevant information in the attention heatmaps and allows us to interpret the decision-making process of eDA3-X agents. The eDA3-X mainly comprises three modules: state embedder, transformer encoder, and DRL head. eDA3-X first expresses the observation in the saliency vector through the MHA mechanism inside its transformer encoder and propagates the saliency vector to the DRL head, as shown in Figure 1(a,b, respectively.  
In state embedder, observation matrices of shape \(N_{\text{C}}\times R_{\text{X}}\times R_{\text{Y}}\) are projected to \(C\times\left\lfloor\frac{R_{\text{X}}}{P}\right\rfloor\times\left\lfloor\frac{ R_{\text{Y}}}{P}\right\rfloor\) using a convolutional neural network with kernel size of \(P\), where \(C>0\) is the length of saliency vector, and \(P>0\) is _patch size_. The matrices are flattened and concatenated with the saliency vector \(\mathbf{v}^{\text{sal}}\in\mathbb{R}^{C}\) to produce matrices in the shape of \((\left\lfloor\frac{R_{\text{Y}}}{P}\right\rfloor\cdot\left\lfloor\frac{R_{ \text{Y}}}{P}\right\rfloor+1)\times C\). _Position embedding_\(P^{\text{pos}}\), which is a set of trainable parameters in the shape of \((\left\lfloor\frac{R_{\text{Y}}}{P}\right\rfloor\cdot\left\lfloor\frac{R_{ \text{Y}}}{P}\right\rfloor+1)\times C\), is then appended to the matrices for the attention calculation.  
In the transformer encoder, the matrices undergo the norm, MHA, and the multi-layer perceptron (MLP) layers for \(L\) times, as shown in Figure 1(a). The similarities of each sequence in the matrices, derived from embedded observation and saliency vector, are obtained in MHA calculation as explained in Equation (3). In other words, eDA3-X agents' focus in their observations is interpretable by comparing the intensity of attention weight based on similarities between the saliency vector and other elements in observation. The remaining calculation in the transformer encoder is identical to that of MAT-DQN [17]. Note that the shape of matrices remains the same as \((\left\lfloor\frac{R_{\text{Y}}}{P}\right\rfloor\cdot\left\lfloor\frac{R_{ \text{Y}}}{P}\right\rfloor+1)\times C\) before and after the process in transformer encoder.  
Only the saliency vector in the matrices from the transformer encoder are extracted and fed to the DRL head, as shown in Figure 1(b). Unlike in MAT-DQN [17], any deep neural  
Figure 2: eDA3-X architecture, based on the DA3-X architecture [18].  
network can be applied to the DRL head in eDA3-X using arbitrary reinforcement learning algorithm, such as RAINBOW [19], DDPG [20], or IQN [21] (hence denoted as \(X\) in eDA3-X). The variety in DRL heads is expected to enhance agents' learnability and adaptation flexibility. Indeed, we previously reported flexible adaptation to complex environment by adopting different reinforcement learning algorithm in its DRL head [18].
## 1 Introduction
Here, \(E_{i}\) is the energy consumption for each individual node. The main objective in this paper is to prolong network lifetime via an energy efficient and balancing routing algorithm and our contributions are listed as below:  
1. Given the source to sink node distance \(d\), the optimal multi-hop number and the corresponding individual distance \(d_{i}\) can be determined based on the theoretical analysis of energy consumption under event based and time based traffic model. 2. Based on (1), a Distance-based Energy Aware Routing (DEAR) algorithm is proposed which consists of route setup and route maintenance phases. The distance factor is treated as the first parameter during the routing process and the residual energy factor is the second parameter to be considered. The DEAR algorithm can balance energy consumption for all sensor nodes and consequently prolong the network lifetime. 3. Simulation results and comparisons are provided with discussion details. The rest of the paper is organized as follows. Section 2 introduces some related work of energy efficient routing algorithms. In Section 3 we present relevant network, traffic and energy models. In Section 4, the details of our DEAR algorithm are described based the theoretical deduction and numerical analysis under different models. Performance evaluation and comparison are given in Section 5 and Section 6 concludes this paper.
# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
## 3 Using GCNs for the Detection of Fake News
### Combining Fake Characteristics with Dispersion Information Using GCN
Two graph convolution layers with an intermediate dropout layer to avoid overfitting, a global max pooling layer and a fully connected layer on top are employed in their model to process each graph separately and generate the respective vector representations. These representations are concatenated and are given to a fully connected layer to calculate the label of the conversation. In a different approach that does not employ GCNs but focuses on the news' propagation path, the work in [42] emphasizes the detection of fake news in the early stages of their propagation in social networks. Each propagation path is modeled as a multivariate time series, where a numerical vector at each time-step represents the characteristics of a user who engaged in spreading the news. A time-series classifier that combines Recurrent and Convolutional Neural Networks is employed for classifying the propagation path as fake or not. More specifically, Gated Recurrent Units (GRUs) process the information of the source tweet and its retweets and are maxed pooled to create a vector representation of the news' spreading path. A similar neural network structure, with Convolutional Neural Network units (CNNs) instead of GRUs, generates a second representation of the news' spreading path. The two vectors are concatenated and fed into a multi-layer feedforward neural network that finally predicts the class label for the corresponding propagation path. The works that focus on the detection of rumors in social media combine information from the social graph formed between the users and the conceptual graph that is formed by the content they share and the concepts it conveys. The set of source news items \(S=\{s_{1},s_{2},...,s_{n}\}\) that start the rumors and all the subsequent posts \(P\) from the users of the social network form the rumors dataset. As depicted in Figure 5, the input in this case comprises: i) a social graph \(\mathcal{G_{S}}=V,E\), with users as nodes \(u\in V\) and their social links as (usually undirected) edges \(e\in E\) and ii) a set of rumor propagation graphs in the form of \(\mathcal{G_{S}}=V,E_{i}\), each one corresponding to the propagation of a news item \(s_{i}\) in the social network. A directed edge \(e=(u\to v)\in E_{i}\), denotes that user \(u\in V\) shared/reposted/commented/responded \(s_{i}\), influenced by user \(v\in V\). The adjacency matrix of this graph is denoted with \(A_{i}\). The text embeddings extracted from \(s_{i}\) and the posts associated with it constitute the feature matrix \(X_{i}\) of the rumor propagation graph. Once the graph is constructed, the convolution and classification steps are the same as before. The classification task can be binary (e.g., rumor or not) or multi-class by distinguishing between true or false rumors, non-rumors and unverified ones.