esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
To develop a comprehensive methodology for designing a harvesting robot, a multi-faceted research approach was implemented, structured into three main stages: an exhaustive literature review, an analysis of mechanical components, and an analysis of electronic components.  
The literature review focused on several key aspects. First, existing harvesting robot designs were examined, particularly their main components, such as computer vision systems, robotic arms, and harvesting tools. Various design approaches were evaluated, highlighting their advantages and disadvantages in terms of efficiency, precision, and adaptability to different types of fruit crops.  
Second, the operational capabilities of existing harvesting robots were explored in detail. This included an in-depth examination of their abilities to identify and select rip fruits, navigate uneven agricultural terrain, and adapt to various climatic conditions. Special attention was given to the role of artificial intelligence and machine learning systems in enhancing accuracy in fruit identification and real-time decision-making.  
Additionally, the technological and operational limitations documented in the literature were analyzed. This encompassed challenges such as obstacle detection and avoidance, energy management for prolonged operations, and component durability in harsh agricultural environments. Identifying these constraints was crucial for understanding the areas requiring improvement and for setting clear objectives in the design of the new harvesting robot.  
The literature review also identified current trends in agricultural robot research and development. Significant advances were noted in the use of advanced sensors, automation technologies, and artificial intelligence algorithms. These trends suggest a shift towards more autonomous and precise systems, capable of operating with minimal human intervention and adapting to diverse field conditions.  
Finally, the review underscored areas that require further attention and development in future research. These areas include improving the energy efficiency of robots, developing more robust navigation systems, and integrating communication technologies for the coordination of multiple robots in harvesting tasks. Additionally, the need for more in-depth studies on the interaction between robots and plants was identified, aiming to minimize crop damage during harvesting.
# A Blockchain Network Admission Control Mechanism Using Anonymous Identity-Based Cryptography
## 2 Related Works
### SM9 Signature  
The SM9 signature process consists of three main parts: private key generation, signature creation, and signature verification. We introduce it briefly.  
Private Key Generation:The KGC generates a random number \(ks\in[1,N-1]\) as the master private key for signing. It then computes an element \(P_{pub-s}=[ks]P_{2}\) as the master public key for signing. The signature master key pair is \(\left(ks,P_{pub-s}\right)\). The KGC keeps \(ks\) secret while publicly announcing \(P_{pub-s}\). The KGC selects and publicly announces a one-byte identifier \(hid\), representing the signature private key generation function. Let the identity of user A be \(1D_{A}\). To generate [PERSON]'s signature private key \(ds_{A}\), the KGC first calculates \(t_{1}=H_{1}(ID_{A}||hid,N)+ks\). Then, the KGC calculates \(t_{2}=ks\cdot t_{1}{}^{-1}\)\(mod\)\(N\), followed by \(ds_{A}=[t_{2}]P_{1}\).  
Signature:The message to be signed is M. User \(A\) first calculates \(g=e\Big{(}P_{1},P_{pub-s}\Big{)}\). The user generates a random number \(r\in[1,N-1]\) and calculates \(w=g^{r}\). Then, \(d=H_{2}(M||w,\ N)\) is calculated. The user computes \(l=(r-d)\)\(mod\)\(N\). Finally, \(S=[l]ds_{A}\) is calculated, and the signature for M is \((h,S)\).  
Signature Verification:Upon receiving the signature \((h,S)\), verifier X checks its validity as follows: X calculates the element \(g=e\Big{(}P_{1},\ P_{pub-s}\Big{)}\). CKGC then computes \(t_{1}=g^{h}\). It calculates \(h_{1}=H_{1}(ID_{A}||hid,\ N)\). Then, X computes \(\dot{P}=[h_{1}]P_{2}+P_{pub-s}\) and the element \(u=e(S,P)\). X calculates \(w^{\prime}=u\cdot t\) and then \(h_{2}=H_{2}(M||w^{\prime},\ N)\). If \(h_{2}=h^{\prime}\), the verification is successful; otherwise, it fails.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 3 Deep Keypoint-Based Monocular SLAM
### Deep Keypoint Meta-Learning  
#### 3.1.1 Feature Extractor Network  
Inspired by SuperPoint [14], our deep keypoint extractor also leverages VGG16 [46] as the backbone architecture. Unlike SuperPoint, we introduce Batch Normalization layers after each Convolutional Neural Network (CNN) to encourage training convergence. After this backbone, there are decoders for a keypoint detector and descriptor, each consisting of multi-layer CNNs. The first module, i.e., the detector decoder, produces a probability mask matching the input image size. We posit that keypoints are situated at positions where the mask value surpasses a certain threshold. The second module, i.e., the descriptor decoder, predicts feature descriptors for keypoints, which are then normalized to a dimension of 256 using the L2 norm.
# Integration of the 3D Environment for UAV Onboard Visual Object Tracking
## 2 Related Work
In addition, the reconstructed object is associated with a velocity, inferred from the optical flow of the object, which is afterward associated with tracklets, thus enabling the authors to tackle occlusion situations and missing detections. Most related to our work is the approach presented in [38], where the authors developed an MOT pipeline for UAV scenarios that also benefits from a 3D scene reconstruction for estimating the object location in 3D. In contrast to our work, the authors rely on the tracking-by-detection paradigm by leveraging RetinaNet [39] for detecting objects in the image sequence. They generate tracklets on image-level by integrating visual cues and temporal information to reduce false or missing detections. By projecting the image-based positions of detected objects on the estimated ground plane--inferred through visual odometry and multiview-stereo--the framework is able to assess their 3D positions. However, by using an object detector, the authors are only able to track object classes known by the object detector. In contrast, model-free trackers, i.e., SOTs, are able to track arbitrary objects. We are convinced that tracking applications such as single object visual tracking from a UAV would also benefit from a shift towards a detection-by-tracking paradigm by incorporating 3D information. In this paper, we apply a model-free single object visual tracker, implying that the tracker can only track a single object and starts with a blank appearance model--without an offline/pretrained appearance model. Regardless of the method used for training the tracker--i.e., offline, online--an appearance model of the object is used to locate the object in the image space. Here, we consider the state-of-the-art visual trackers ATOM [27] and DiMP [28] for appearance modeling. Important for enabling the 2D to 3D mapping is a 3D representation of the observed scene. To this end, a Structure-from-Motion (SfM) or visual Simultaneous Localization and Mapping (SLAM) approach can be leveraged. SfM is a photogrammetric technique that estimates the 3D structures of a scene based on a set of images taken from different viewpoints [40; 41; 42; 43; 44; 45]. Visual SLAM, similarly to SfM, reconstructs 3D camera poses and scene structures by leveraging specific properties of features in ordered image sets [46; 47; 48]. In addition, UAVs can improve the robustness of the reconstruction by associating an Inertial Measurement Unit (IMU) with the corresponding SfM or Visual SLAM algorithm [49; 50; 51; 52; 53; 54; 55]. However, in this work, an established software for SfM called COLMAP [40] is used to extract camera poses in the scene space and to reconstruct the static scene. We estimate the state of the object in 3D space by relying on a state estimator, i.e., a particle filter [56]. For evaluating our approach, the publicly available dataset AU-AIR [32] is employed. The dataset is carefully further edited resulting in the AU-AIR-Track dataset, to best reflect prototypical occlusion situations from a low-altitude oblique UAV perspective.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 1 Introduction
In this paper, we propose a sensitivity analysis algorithm for HMMs using a simplified matrix formulation directly from the model representation based on a recently proposed technique called the Coefficient-Matrix-Fill procedure [26]. Until recently, sensitivity analysis in HMMs has been performed using Bayesian network sensitivity analysis techniques. The HMM is represented as a dynamic Bayesian network unrolled for a fixed number of time slices, and the Bayesian sensitivity algorithms are used. However, these algorithms do not utilize the HMMs' recursive probability formulations. In this work, a simple algorithm based on a simplified matrix formulation is proposed. In this algorithm, to calculate the coefficients of the sensitivity functions, a series of Forward matrices \(F^{k},k=1,...,t\) are used, where \(k\) represents the time slice in the observation sequence. The matrices (Initial, Transition, and Observation) where the corresponding model parameter \(\theta\) varies are decomposed into the parts independent of and dependent on \(\theta\) for mathematical convenience. This enables us to compute the coefficients of the sensitivity function at each iteration in the recursive probability expression and implement the algorithm in a computer program. These matrices are computed based on a proportional co-variation assumption. The Observation Matrix \(O\) is represented as \(n\times n\) diagonal matrices \(O^{t}\) whose \(\
u\)th diagonal entry is \(P(y_{e}^{t}|x_{v}^{t})\) for each state \(v\) and whose other entries are 0 at the time \(t\) of the observation sequence. The proposed algorithm is computationally efficient as the length of the observation sequence increases. It computes the coefficients of a polynomial sensitivity function by filling coefficient matrices for all hidden states and all time steps. The paper is organized as follows. In Section 2, the background on HMM and Sensitivity Analysis in HMM is presented. The details of the proposed algorithm for the filtering probability sensitivity function are explained in Section 3. In Section 4, the sensitivity analysis of the smoothing probability using the proposed algorithm is discussed. Finally, the paper is concluded with a summary of the results achieved and recommendations for future research works.
## 3 Methodology
### Time Series Classification Based on the Bi-LSTM  
A multi-layer LSTM in two opposite directions [65] is used in this study to classify multiple categories of land cover through spatio-temporal data processing and multi-label land cover. The LSTM-based land cover classification model is shown in Figure 3. In Figure 3, the classification models are expanded in chronological order. The output layer originally existing in the LSTM network is removed. The output (\(h^{1}\), \(h^{2}\),..., \(h^{T-1}\), \(h^{T}\)) in the hidden layer is input into an averaged pooling layer to obtain a vector h without time information [66]. So far, an LSTM network was established with the output layer removed and added to the average pooling layer, called a Forward LSTM. At the same time, we also use a reverse version of the LSTM called the Backward LSTM. The two structures are basically the same, except that the Backward LSTM network requires the input data to be input in reverse order of the time series, and the output of this layer is represented by \(h_{b}\). Forward LSTM speculates the information based on the previous information, and Backward LSTM can push back the previous information by the following information. Bi-LSTM is a combination of Forward LSTM and Backward LSTM. This two-way information extraction is helpful for classification. Existing experiments have shown that the classification effect of Bi-LSTM is better than that of LSTM classification [67].  
Figure 2: Basic structure of an LSTM. \(S_{t}\) represents the memory at time \(t\), \(U\) represents the weight of the input layer to the hidden layer, \(W\) represents the weight of the hidden layer to the hidden layer and \(V\) represents the weight of the hidden layer to the output layer.  
The land cover classification model takes the time series of multivariate quantitative remote sensing products as the input, and its output is the model's estimate of the category label. It should be noted that the output of the ordinary RNN network or the LSTM network is a sequence of the same length as the input sequence, which is obviously inconsistent with the problem of the research. To this end, changes need to be made based on the LSTM model to adapt the output to the land cover classification problem.  
To perform the task of classification, we construct a deep architecture with multiple layers of LSTMs stacked together, which will allow the extraction of advanced nonlinear time features in remote sensing time series. The proposed architecture is similar to the CNN network that combines several convolutional layers. Since the LSTM itself does not perform the task of class prediction, a softmax layer is added at the top of the LSTM network to perform multi-category predictions follow previous studies [68]. The category corresponding to the largest value in the softmax neuron is the final result of the prediction.
# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
## 2 Methods
### NAR Dynamic Neural Network  
The NAR model is a kind of dynamic neural network, which specializes in analyzing and processing time series. Compared with the static neural networks, such as BP and RBF neural networks, the current output content in the dynamic neural network can be assigned as the input of the neural network layer during the next period, thus providing a basis and reference for the next period [38], realizing a constant update of parameters and more reliable prediction of the time series.  
At the same time, different from the NARX model, the NAR model only has the output, so it only needs to analyze the time series of a certain indicator [39]. In the NAR model, there are fewer requirements for data information and fewer restriction factors, so the prediction can be completed with only one set of data; this makes the prediction process simpler and more convenient, even when the reference data are insufficient [40].  
The mathematical equation of NAR dynamic neural network can be constructed as follows:  
\[y(t)=f[y(t-1),y(t-2),\cdots,y(t-d)] \tag{3}\]  
where \(d\) is the delay order, and \(y(t)\) is the current predicted value.  
The specific structure of the model is shown in Figure 1, where \"4\" is the delay order, \"15\" is the number of hidden layer neurons, W is the connection weight, and b is the threshold.  
The output \(H_{j}\) of each neuron in the NAR neural network is calculated according to Equation (4).  
\[H_{j}=f(\sum_{i=1}^{n}w_{ij}x_{i}+a_{j}),j=1,2,\ldots,l \tag{4}\]  
where \(i\) is the number of input data, \(l\) is the number of neurons in the hidden layer, \(f\) is the activation function of the hidden layer, \(x_{i}\) is the \(i\)-th input data, \(w_{ij}\) is the connection weight between the \(i\)-th output delay signal and the \(j\)-th neuron in the hidden layer, and \(a_{j}\) is the threshold of the \(j\)-th hidden layer neuron.  
The output layer performs linear calculation according to the output \(H_{j}\) of the hidden layer and calculates the output \(O\) of the neural network.  
\[O=f(\sum_{j=1}^{l}H_{j}w_{j}+b) \tag{5}\]  
where \(w_{j}\) is the connection weight between the \(j\)-th neuron in the hidden layer and the output layer neuron, and \(b\) is the output layer neuron threshold.
# High-Fold 3D Gaussian Splitting Model Pruning Method Assisted by Opacity
## 2 Related Work
### Vector Quantization  
Vector Quantization (VQ) [34; 35] is a widely used data compression technique applied in various domains, such as image compression, speech coding, and neural network model compression. It compresses data by mapping a large number of data vectors to a limited set of vector codebooks. The core idea is to replace the input vectors with their nearest neighbors from a pre-defined codebook, effectively reducing data storage requirements. Training the codebook ensures that it generalizes well to represent the original data [36]. [PERSON] et al. [37] explored the application of vector quantization in speech coding, proposing techniques to enhance audio quality at low bitrates. [PERSON] et al. [20; 21; 38] introduced a vector quantization method to optimize the memory efficiency of 3D scene rendering models, achieving reduced storage with high visual fidelity. Current works have applied vector quantization to compress the attribute parameters of 3D Gaussian models, demonstrating its feasibility. In this paper, we employ vector quantization only on the spherical harmonic coefficients.
## 3 Results and Discussion
### Comparison between Measurements and Predictions
Moreover, Figure 4 illustrates that errors appear to be distributed alike at low and high frequencies, while a better accuracy can be achieved in the middle frequencies (250 Hz-1 kHz). Figure 5 shows a probability density function of error distributions in the estimation of isolation curves across all frequencies. It is noticeable that the highest error density is in the \((-5,5)\) dB range. The graph shows a peak around 0 dB that reveals the goodness of the model at predicting airborne sound insulation curves with convenient results.
# SEA and GATOR 10 Years Later
## 5 Function of the SEA and GATOR in Nutrient Sensing and Responding
### GATOR1 Interaction with SAM Sensor, SAMTOR  
The SAM sensor, SAMTOR, binds to GATOR1 during SAM or methionine deprivation, and negatively regulates mTOR1 activity [100]. The component of GATOR1 that interacts with SAMTOR is currently unknown. In the presence of SAM, this metabolite occupies its binding pocket in SAMTOR, which disrupts the interaction of an amino acid sensor with GATOR1, promoting mTOR1 activity. SAMTOR and GATOR1 interactions are dependent on KICSTOR (see below). When SAMTOR is bound to SAM, it dissociates from GATOR1-KICSTOR, thus inhibiting GATOR1 and promoting mTOR1 activation [101]. On the other hand, methionine starvation promotes interaction between SAMTOR and the GATOR1-KICSTOR complex, but weakened the interaction between GATOR1 and GATOR2, thus leading to mTOR1 suppression [100]. SAM levels can be affected by the availability of vitamin B12. Mice NPRL2 KO embryos have significantly reduced methionine levels and demonstrate phenotypes reminiscent of B12 deficiency [20]. It is currently unknown whether methionine can be sensed directly. Interestingly, leucine can also signal to mTOR1 through its metabolite, acetyl-coenzyme A, but in a RAG-independent and cell-specific manner [102].  
In a recent study, [PERSON] laboratory investigated the potency of each amino acid to stimulate mTOR1 in MEF or HEK293 cells [65]. Ten amino acids were able to re-stimulate mTOR1 and promote its lysosomal localization. Glutamine and asparagine signal to mTOR1 through a RAG-independent mechanism via ADP-ribosylation factor ARF1. Eight amino acids (alanine, arginine, histidine, leucine, methionine, serine, threonine and valine) filter through RAGs. While three cytoplasmic sensors for leucine, arginine and methionine (SAM) have been identified, it is not known whether the other five amino acids also have their specific sensors and whether they will interact with GATORs.
# Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services
## 5 Experimental Evaluation
### Results
#### 5.3.2 Maximum Latency  
The test aims at measuring the latency registered by the prototype of the 5 GT System to be deployed in \"High traffic\" conditions. During the tests, the 8 CTDs were activated and provided NB-IoT transmissions at a time interval of 120 s. Table 4 details the minimum and maximum values of latency obtained for each CTD during the experiments. The results clearly show that the maximum mean latency related to the data published on the ICON platform is equal to 5.5 s, which is reasonable. In fact, although NB-IoT has been designed for delay-tolerant applications [27], certain applications (e.g., alarms) may require a reasonably strict delay profile. For devices supporting such applications, a maximum delay requirement of 30 s is still appropriate in normal conditions for the uplink when measured from the application 'trigger event' to the packet being published to the terrestrial oneM2M Platform. The results obtained during our experimental evaluation are far below 30 s, which confirms the good performance of the proposed platform.
# Smart Helmet and Insole Sensors for Near Fall Incidence Recognition during Descent of Stairs
## 1 Introduction
On construction sites, workers are frequently exposed to fall risks and thus it is important to detect foot areas and detect changes in the balance of both feet [13]. [PERSON] (2007) and [PERSON] et al. (2017) reported that potential sources of falls are bad interactions between the foot and floor-surface, with changes of workers' gait patterns on construction sites [14; 15]. [PERSON] et al. (2015) and [PERSON] et al. (2016) developed an insole-type fall detection system using a pressure sensor and conducted experiments to detect falls [16; 17]. They proposed a system for evaluating falls by analyzing changes in pressure on the sole surface. However, the FSR sensor used by them has low durability. In particular, low durability can be a problem in detecting worker falls, because there are more cases of carrying heavy loads on construction sites than in everyday life, and it may not be suitable for detecting falls due to sensor damage or distortion caused by external impact. In general, all construction workers must wear a safety helmet and safety shoes [18]. Research conducted by [PERSON] (2012) reported that wearing appropriate safety equipment contributed to a reduction of fall accidents of more than 30% [19]. However, existing safety helmets and shoes are used only to protect construction workers against external shocks. In previous studies, safety helmets and shoes with IMU sensors were developed [20; 21; 22; 23]. The purpose of our research was to evaluate fall incidence by simulating three experimental conditions using a three-axis accelerometer sensor and pressure sensor. In our previous studies, we developed a smart helmet and textile-based smart insole sensor [21; 24; 25]. Based on the data collected from the developed sensors, we analyzed whether our proposed sensor could distinguish between falls and non-falls.