esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Analysis
### Memory Requirement  
In our system, the memory requirements mainly come from the following aspects:  
1. Data caching:  
In relay nodes and terminal devices, some of the transmitted data need to be cached to support multi-hop transmission and related processing operations. Specifically, each relay node needs to store the data from the previous hop until they are forwarded to the next hop. Assuming that the amount of data processed by each relay node is D bytes and there is one relay node in the system, the total memory requirement of the relay nodes is D bytes.
2. Coding information storage:  
In order to realize additional coded modulation functions, coding parameters and related code word information need to be stored. For orthogonal codes such as Walsh codes, the memory requirement is related to the code length and the number of code words. For example, for a Walsh code of length P, it is necessary to store \(P\times\log_{2}P\) bits of code word information.
3. Correlation computation storage:  
When multiple correlation computations are performed, the intermediate results need to be stored temporarily. Assuming that each correlation calculation involves \(U^{2}\) data points, each calculation unit needs to store \(U^{2}\) bytes of intermediate results.  
We quantitatively analyzed memory requirements to assess their impact on system resources. The details are as follows:  
1. Relay node memory requirement: assuming that the amount of data handled by each relay node is 1024 bytes (1 KB) and there is one relay node in the system, the total memory requirement of the relay nodes is 1 KB.
2. Coding information storage: for a Walsh code with length 64, it needs to store \(64*\log_{2}64=384\)bits, i.e., 48 bytes.
3. Correlation calculation storage: assuming that each correlation calculation involves 200 data points and each data point is 1 byte, then each calculation unit needs to store an additional 200 bytes of intermediate results.  
The above memory requirements are acceptable in modern communication systems. For example, modern relay nodes are typically equipped with large memory capacities (e.g., 128 MB or more), so the 1 KB storage requirement represents a very small fraction (about 0.0007%) of their total storage capacity.
## 2 Materials and Methods
### Efficacy of Ultrasonic Treatment at Laboratory Conditions  
We adopted a technology employing acoustic waves generated by an ultrasound transducer, the Larvasonic apparatus (New Mountain Innovations Inc, Old Lyme, NY, USA) [5]. The transducer distributes acoustic energy uniformly in the horizontal plane and has a 360-degree volume of vertical coverage. In our study, the tested frequency range was 18-30 kHz. The transducer elements were made of piezoceramics with an output of 100 W. Experiments were conducted in an open fish tank 87 \(\times\) 44 \(\times\) 44 cm\({}^{3}\) filled with water up to a height of 20 cm, 100 mosquito larvae (instars I, II, III, and IV, or pupae) were transferred to a net (150 \(\upmu\)m mesh size; 2.3 \(\times\) 44 \(\times\) 44 cm\({}^{3}\)) and placed inside a fish tank at 13 test points of distance (0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, and 60 cm) from the transducer immediately after starting the exposure to Larvasonic, lasting 30, 60, 90, 120, 150, 180 s. Every experiment was replicated 5 times with new larvae or pupae. After each replicate, the number of dead mosquito larvae or pupae was noted. Non-target adults of the cyclopod copep _M. formosanus_ were tested independently at the same distance and exposure time described above for _Ae. aegypti_ larvae.
# E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
## 4 Experiments  
### Data Acquisition  
Data for our study were sourced from actual excavation sites, as depicted in Figure 2a. The data collection involved an SY870 excavator [18] performing earthmoving tasks, with the operator ensuring consistent excavation criteria and ground leveling post-operation. The SY870 excavator is manufactured by Sony Group, headquartered in Changsha, China. The excavator's position remained fixed during the tasks, and the terrain was restored after each session to maintain consistent conditions for every new excavation. The SY870 is a hefty machine with a 4.5 cubic meter bucket capacity. In the unloading phase, excavated soil was loaded into mining trucks.  
Throughout the excavation processes, the excavator's location was kept constant. The operator used a uniform standard to determine the end of the excavation process. The goal was to keep the post-excavation ground as level as possible and to ensure a constant excavation speed. To avoid operational anomalies, the operator restored the terrain after each excavation to ensure the initial conditions for subsequent operations were as consistent as possible. Figure 2b shows the work area of the excavation operation, with Area A being the excavation zone and Area B being the unloading zone. The width of the spoil platform at the excavation site was 13 m, and the work area was 6.5 m by 1.4 m by 4 m.  
To simulate actual operations, radar devices were installed on both sides of the excavator's cabin and boom to collect real-time data about the surrounding environment and terrain. The excavator's LiDAR is the Ouster OS1. The Ouster OS1 is manufactured by Ouster, Inc., headquartered in San Francisco, CA, USA. This is a high-performance LiDAR with a detection range of up to 100 m and an angular sampling accuracy of \(\pm\)0.01\({}^{\circ}\) vertically and \(\pm\)0.01\({}^{\circ}\) horizontally. The excavator's four joint angles are fitted with IMU SYD T500 sensors. The manufacturer of the IMU SYD T500 sensors is SYD Dynamics, based in Odense, Southern Denmark. This is an integrated inertial measurement unit that incorporates multiple sensors, capable of outputting measurements from a 3-axis gyroscope and a 3-axis accelerometer with a timestamp resolution of less than 1 us. During operations, these data were stored at a frequency of 50 Hz in Rosbag packages. The dataset includes the angular velocity of each joint of the excavator, joint force, piston rod speed, volume of soil excavated, ground support force, and LIDAR point clouds. The operator's excavation trajectory, followed during the operation, was transmitted to a simulation platform (Unity) via the UDP protocol in Matlab 2023a to acquire data on the excavation process in a simulated environment. Figure 2c illustrates the terrain of the work area after the completion of the excavation in the real scenario. Figure 2d displays the terrain of the work area after the excavation in the simulated scenario, which is essentially consistent with the terrain in the real scenario.
## 3 Results and Discussion
### Raw Annotated Data (RD)
The results showed that MA increased from 0.572 for the AM1 to 0.655 for the BM model, precision increased from 0.239 for CM to 0.605 for the BM model, and F1-score increased from 0.261 for CM to 0.393 for the BM model. Considering GA metrics, CM shows the best performance value of 0.773. Furthermore, the confusion matrix showing the outcome of the pixel distribution after classification for the FRNB raw annotated dataset is depicted in Figure 8. The effect of augmenting the model had caused no significant change. The FP and TN only increased and reduced by 1%, respectively. In contrast, the effect of balancing only caused a substantial change in the evaluation parameters. The TP increased by 6%, while the TN reduced by  
Figure 6: Evaluation metrics for FRSB for RD. 19%. Similarly, FP reduced by 8% while the FN increased by 19%. Augmenting and then balancing the dataset had an insignificant effect on the balanced-only outcome. The evaluation metrics outcome shows that augmentation reduced the performance of the models while balancing the dataset alone significantly improved the model's performance. This corroborated studies by [PERSON] et al. (2020), where IOU dropped after augmentation for the testing dataset. Lastly, the GA, precision, TNR, F1-score, and TPR are highest for all models developed with the BD dataset.
## 2 Related Work  
### Spatial Modulation  
We consider a single base station with \(N_{T}\) transmit antennas that communicates with a single user equipped with \(n_{r}\) receive antennas. In the conventional SM [2], a single signal symbol, \(s_{k}\) is transmitted from a single antenna, where both the signal symbol's index and the antenna index carry information. As such, the system equation is given by:  
\[\mathbf{y}=\mathbf{h}_{i}s_{k}+\mathbf{n} \tag{1}\]  
where \(\mathbf{h}_{i}\) is the \(i\)-th column of the channel matrix \(\mathbf{H}\) of size \(n_{r}\times N_{T}\), \(\mathbf{n}\) is the additive white Gaussian noise vector of length \(n_{r}\) and \(\mathbf{y}\) is the received vector of length \(n_{r}\). The elements of \(\mathbf{H}\) are independent and follow a centered circularly symmetric Gaussian distribution with variance of 1. The signal symbol \(s_{k}\) is drawn from a conventional phase shift keying (PSK) or quadrature amplitude modulation (QAM) constellation \(\Omega\), whose size is \(L\), and \(q=log_{2}(L)\) is the number of bits conveyed by each signal symbol.
###### Abstract  
Raman spectroscopy has been widely applied in numerous fields including bioanalysis, disease diagnosis, and molecular recognition, owing to its unique advantages of being non-invasive, rapid, and highly specific. However, the acquisition of Raman spectral data is often susceptible to various noise interferences, such as shot noise from the internal detector and dark current noise from the instrument. As a result, the weak Raman signals typically become arduous to discern, which affects the localization and identification of characteristic spectral peaks. This study investigates variational mode decomposition (VMD), empirical wavelet transform (EWT), and integrated encoder-bidirectional long short-term memory (EBiLSTM) modules to propose a neural network algorithm for adaptive denoising of Raman spectra. By combining VMD and EWT, the Raman spectra are decomposed into several sub-sequences, which solves the problem of mode mixing between high-frequency signals and noise in empirical mode decomposition, and significantly reduces the complexity of the original Raman spectral lines. The correlation coefficient between each modal component and the original signal is calculated along with the zero-crossing rate index to categorize the noise and signal sequences. Leveraging the linear differences between the ideal spectral lines and the noisy spectral curves, an encoder-bidirectional long short-term memory (EBiLSTM) denoising network is constructed for hierarchical denoising to extract valid spectral feature information from the high-frequency components, realizing refined adaptive denoising of the Raman spectra. Industry standard objective evaluation metrics on the signal-to-noise ratio and root mean square error are utilized to conduct simulation experiments comparing state-of-the-art algorithms, including empirical mode decomposition, VMD, sliding window averaging, and wavelet thresholding. The experimental results demonstrate that the Raman spectral denoising algorithm combining variational mode decomposition and neural networks improves the denoising performance by 13.38% to 72%, exhibiting higher accuracy and reliability.  
variational mode decomposition; empirical wavelet transform; Raman spectroscopy; hierarchical denoising +
Footnote †: journal: _Applied_
## 4 Results  
### Features Metrics and Extraction  
Feature extraction can be viewed as finding a set of vectors that represent an observation while reducing the dimensionality. In LULC classification, it is desirable to extract features that are focused on discriminating between classes. The development of feature extraction has been one of the most important research subjects in the field of LULC analysis and has been studied extensively. In this study, we concentrate on feature extraction for SAR and optical images based on LULC classification task, such features included geoscience auxiliary features, spectral features, texture features and relevant context features. The principal component transformation is based upon the global covariance matrix. It often works as a feature reduction tool because classes are frequently distributed in the direction of the maximum data scatter. Discriminant analysis is a method which is intended to enhance separability.
## 3 Results
### Studies' Characteristics  
The variables analyzed in the 15 selected studies are mentioned in Table 3. Three studies were carried out in Brazil [18; 22; 63], four in China [61; 64; 65; 21], two in Lithuania [20; 36], two in the United Kingdom [59; 66], one in Korea [19], one in Iraq [62], one in Taiwan [60], and one in Turkey [68]. Two studies were randomized controlled trials [68; 69], two studies were quasi-experimental designs [21; 22], while eleven studies were pre-experimental designs [18; 19; 20; 36; 60; 61; 62; 63; 64; 65; 66].
# Analysis of AMI Communication Methods in Various Field Environments
## 6 Discussions
### Discussions on the Wireless Communication
Because the SNR values of Figure 33a,b are positive, safe communications can also be expected for both cases. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Environment Area**} & \multicolumn{4}{c}{**Wi-SUN**} & \multicolumn{2}{c}{**ZigBee**} \\ \cline{2-7}  & **EPD (14 dBm)** & **EPD (23 dBm)** & **AR** & **EPD** & **AR** \\  & \(\mathbf{d_{0}}\) (Meters)** & & \(\mathbf{a}\) (dB/\(\mathbf{log(d)}\)) & \(\mathbf{d_{0}}\) (Meters) & \(\mathbf{a}\) (dB/\(\mathbf{\log(d)}\)) \\ \hline \multirow{4}{*}{Overhead} & Residential & \(118\) & \(188\) & \(-44.4\) & \(94.8\) & \(-44.1\) \\  & Commercial & \(463\) & \(978\) & \(-27.7\) & \(371\) & \(-25.9\) \\ \cline{1-1}  & Rural & \(322\) & \(621\) & \(-31.5\) & \(139\) & \(-38.9\) \\ \cline{1-1}  & Factory & \(349\) & \(685\) & \(-30.7\) & \(217\) & \(-30.8\) \\ \hline \multicolumn{2}{c}{Underground} & \(31.8\) & \(39.6\) & \(-93.9\) & \(37.0\) & \(-79.4\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: EPD and the AR for the wireless communications. Figure 33: Graphical simulation of the received power for the 920 MHz Wi-SUN modems (Site 1-2, Overhead Residential Area). (**a**) Power map with one node, in which data concentration unit (DCU) and wireless modems are attached below the transformer. (**b**) Power map with two nodes, in which the wireless modems of the second node acts as a repeater.
## 2 Methods
### Quantum Proxy Signature Scheme
#### 2.3.2 Authorizing Phase  
In authorizing phase, [PERSON] generates the warranty allowing [PERSON] to execute the proxy signature.  
**Step 1** [PERSON] produces a quantum state \(|\varphi\rangle_{W}\) with \(n2\) qubits, which contains the information of [PERSON]'s and [PERSON]'s identification and the warranty of proxy signature. In addition, \(|\varphi\rangle_{W}\) is described as  
\[|\varphi\rangle_{W}=\{|\varphi_{1}\rangle_{W},|\varphi_{2}\rangle_{W},\ldots, |\varphi_{i}\rangle_{W},\ldots,|\varphi_{n2}\rangle_{W}\}, \tag{14}\]  
where \(|\varphi_{i}\rangle_{W}=\frac{1}{\sqrt{2}}(|0\rangle+m|1\rangle)\) with \(m=1\) or \(-1\), \(i=1,\ldots,n2\). Then he combines \(|\varphi\rangle_{M}\) with \(|\varphi\rangle_{W}\) to acquire a new quantum state \(|\varphi\rangle_{M^{\prime}}\) with  
\[|\varphi\rangle_{M^{\prime}}=(|\varphi\rangle_{M},|\varphi\rangle_{W})=\{| \varphi_{1}\rangle_{M^{\prime}},\ldots,|\varphi_{i}\rangle_{M^{\prime}},\ldots,|\varphi_{n}\rangle_{M^{\prime}}\}, \tag{15}\]  
Figure 2: Schematic of the designed quantum proxy signature scheme. [PERSON], [PERSON], [PERSON], and [PERSON] are the original signer, the proxy signer, the verifier and the arbitrator, respectively. Notably, blue dashed box represents the initializing phase and authorizing phase, and green represents the signing phase and orange represents the verifying phase.  
which contains \(n=n1+n2\) qubits. We assume the dimension \(n\) of \(|\varphi\rangle_{M^{\prime}}\) to be large enough, which enables small enough error probability of two rounds of comparisons for any two unknown qubit states and failure probability of the validity verification for the completed signature in the verifying phase.  
**Step 2** [PERSON] separately encrypts two copies of the new quantum state \(|\varphi\rangle_{M^{\prime}}\) with \(K_{AC}\) and \(K_{CT}\) and gets \(|\varphi\rangle_{M^{\prime}}^{1}=E_{K_{AC}}(|\varphi\rangle_{M^{\prime}})\) and \(|\varphi\rangle_{M^{\prime}}^{2}=E_{K_{CT}}(|\varphi\rangle_{M^{\prime}})\). Next he sends \(|\varphi\rangle_{M^{\prime}}^{1}\) to [PERSON] and \(|\varphi\rangle_{M^{\prime}}^{2}\) to [PERSON].  
**Step 3** After receiving \(|\varphi\rangle_{M^{\prime}}^{1}\), [PERSON] obtains \(|\varphi\rangle_{M^{\prime}}\) by decrypting it and thus she has the authority to help [PERSON] and [PERSON] complete the signature as a proxy signer.
# Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion
## 4 Experiments
### Qualitative Analysis  
Figure 5 presents qualitative prediction examples comparing HiCA-VQA with Hi-VQA. The questions are arranged from left to right in the hierarchical order of their granularity to illustrate their hierarchical dependencies. In the first case, Hi-VQA generated a negative response to the initial question, which propagated to subsequent questions, resulting in cascading negative predictions. In the second and third examples, our method demonstrates improved accuracy in predicting lower-level questions compared to Hi-VQA. This observation underscores that hierarchical answer encoding enables contextually adaptive predictions for questions of varying granularities, thereby enhancing overall prediction accuracy, as previously reported in prior studies.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Proposed Module**} & \multicolumn{6}{c}{**Metrics**} \\ \cline{3-10}  & & **SF** & **CF** & **AL** & **HD** & **Acc** & **F1** & **Pre** & **Rec** \\ \hline \multirow{2}{*}{Hi-VQA [9]} & (a) & ✓ & × & × & × & 32.6 & 31.7 & 70.7 & 32.1 \\  & (b) & ✓ & × & × & ✓ & 33.7 & 29.5 & 80.4 & 30.7 \\ \hline \multirow{2}{*}{con-VQA [29]} & (a) & ✓ & × & × & × & 32.6 & 28.7 & 80.0 & 28.8 \\  & (b) & ✓ & × & ✓ & × & 39.7 & 31.0 & 90.4 & 33.6 \\ \hline \multirow{2}{*}{HiCA-VQA (Ours)} & (a) & × & ✓ & × & × & 38.0 & 33.0 & 67.7 & 32.2 \\  & (b) & ✓ & × & ✓ & ✓ & 36.8 & 32.7 & 68.2 & 32.3 \\  & (c) & × & ✓ & ✓ & ✓ & 39.9 & 49.1 & 69.8 & 34.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The ablation experimental results are presented in the following table. Among them, SF denotes the self-attention fusion module, CF denotes cross-attention fusion, AL denotes the alignment module, and HD denotes hierarchical answer decoders.
## 2 Material and Methods
### Multi-Channel and Multi-Spatial Attention Module
#### 2.4.1 Multi-Channel Attention Layer  
The multi-channel attention layer aims to exploit the inter-channel relationship of a feature map. Each channel in a feature map can be considered as a feature detector, and the channel attention determines which feature is more meaningful for the final diagnosis by assigning different weights to different channels.  
As shown in Figure 3a, spatial information in the given feature map \(\mathbf{F}\in\mathbf{R}^{\mathrm{C}\times H\times W}\) is squeezed by both average and max pooling operations. The results of these two pooling operations can be denoted as \(\mathbf{F}_{avg}^{\mathrm{C}}\) and \(\mathbf{F}_{max}^{\mathrm{C}}\) (both in \(\mathbf{R}^{\mathrm{C}\times 1\times 1}\)), which are then fed into several multi-layer perceptrons (MLP), each having one hidden layer. To reduce the parameter overhead, a reduction ratio \(r_{i}\) is set for the \(i\)th MLP that produces a hidden activation in \(\mathbf{R}^{\mathrm{C}/r_{i}\times 1\times 1}\). The intuition is that MLPs with different reduction ratios could extract more diversified channel attention vectors. The MLPs in multi-channel attention layer are defined as \(MLP_{r_{1}}\), \(MLP_{r_{2}}\),..., \(MLP_{r_{m}}\), where \(m\) is the number of reduction ratios. Outputs of these MLPs are first merged using channel-wise maximization and average operations, and further merged through an element-wise summation, and eventually pass through a sigmoid function to generate a channel attention \(\mathbf{F}_{\mathrm{C}}\). Finally, we obtain a refined feature map \(\mathbf{F}^{\prime}\) through a channel-wise multiplication operation between the channel attention \(\mathbf{F}_{\mathrm{C}}\) and the original feature map \(\mathbf{F}\), as shown in Equation (1).
# A Flexible Session-Based Recommender System for e-Commerce
## 2 Methods and Literature Review
### Tested SBRS Methods and Practical Scenarios
#### 2.5.5 Graph-Based SBRS  
In our project we implemented two SBRS methods using Neo4j. The first method, called pair popularity, models in the graph all item co-occurrences found anywhere in the same session. Having recorded the item P viewed by the user at session step t, the method recommends a list of items for step t+1 based on how often the items co-appeared with P in the training sessions. Later, we proposed the hierarchical sequence popularity (HSP) recommendation method. This method uses a hierarchical representation of item sequences in user sessions to further improve the results and produces significantly better results. To recommend an item, we looked at its frequency of appearance as well as the history of sequences of length 1 or 2 in which this item participated during the training sessions. In the absence of history (i.e., in the first step of a session), items were recommended based on their popularity. Both recommendation approaches can be applied to a wide variety of e-shops regardless of the type of items. The item recommendations that the methods offer can be used as an essential component to automate and improve the identification of related items for the online customer.  
The main advantage of graph-based recommenders is that they quite are effective, but at the same time are efficient when considering the complete operation cycle of SBRS (data gathering, modeling, processing, analysis, filtering). Graph-based SBRS, due to their underlying architecture, can incrementally collect data from an e-commerce website from all ongoing user sessions. Moreover, these data are immediately available to the recommendation algorithm because no training phase is required. Finally, it is easy to integrate new business rules and constraints on demand, something that is more difficult to inherently implement using ML methods.  
Generally, scalability is a critical issue that should be very carefully considered, especially when building SBRS for big data. If a method is very effective, but it requires a substantial amount of training time exceeding the periodic time in which the recommender prediction model should be updated (every day or every week), it is not applicable.