esa-sceva/satcom-chunk-collection huggingface dataset:
# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 1 Introduction
#### Current Study Contributions  
Outlined below are the main contributions of our research:  
* We release a publicly available corpus of Romanian stories comprising 1263 texts and 12,516 paragraphs written by 19 authors. The dataset can be accessed at [[https://huggingface.co/datasets/readerbench/ro-stories](https://huggingface.co/datasets/readerbench/ro-stories)]([https://huggingface.co/datasets/readerbench/ro-stories](https://huggingface.co/datasets/readerbench/ro-stories)) (accessed 12 February 2024). Additionally, this study examines existing methodologies and limitations in authorship attribution, with an emphasis on languages with limited resources, such as Romanian.
* We introduce a hybrid Transformer model that achieves state-of-the-art performance for authorship attribution for Romanian. This model, available at [[https://github.com/readerbench/ro-auth-detect](https://github.com/readerbench/ro-auth-detect)]([https://github.com/readerbench/ro-auth-detect](https://github.com/readerbench/ro-auth-detect)) (accessed 12 February 2024), combines the top predictive linguistic features selected using the Kruskal-Wallis mean rank and contextualized embeddings from a Romanian BERT model to predict the authors. A comparative analysis is conducted with ensemble learning, incorporating seven traditional machine learning classification algorithms based on linguistic features.
* We present a detailed analysis of the distinctions in the authors' writing styles based on the top discriminative linguistic features.
## 4 Key Enabling Techniques
### Multiple Access Techniques
#### 4.2.2 Sparse Code Multiple Access (SCMA)  
SCMA allows non-orthogonal communication of various user signals amongst code and power domains, which may well improve SE and provide stronger connectivity with limited resources. The authors in [90] conducted SCMA to attain an improved link-level performance, delivering additional multiple access capability through reasonable complexity and energy consumption hence, considered SCMA as an enhanced energy-efficient approach while [PERSON] et al. [91] developed a technique for SCMA to increase the downlink throughput of a deeply loaded network. He found that SCMA can increase link adaptation as a result of reduced colored interference and enhanced SE of wireless networks. In [92], the authors derived and implemented SCMA-MIMO by combining MIMO and SCMA technologies to accommodate the ever-increasing number of users, reliability, and SE improvement for the next-generation 5G networks. [PERSON] et al. [93] proposed an efficient and affordable decoding algorithm based on a Bayesian program learning scheme and Monte Carlo Markov Chain (MCMC) which can reduce 60% computation complexity and only 0.5-dB performance loss related to a similar decoding algorithm.
## 8 Introduction to Proprietary and Standards-based Solutions
### Proprietary Technologies
#### 8.1.2 Ingenu  
Ingenu is based on the proprietary random phase multiple access (RPMA) technique with more flexible spectrum regulations, allowing higher throughput and capacity [20]. The solution is proprietary in the sense that the company is the sole developer and manufacturer of the hardware. It uses a direct sequence spread spectrum technique with a peak data rate up to 80 kbps. Ingenu operates in the 2.4 GHz band, which gives it a shorter range than Sigfox and LoRa, and also encounters more propagation loss from obstructions, like water or packed earth. The 2.4 GHz band is widely used by many other personal and local area network technologies such as Wi-Fi, Bluetooth, and ZigBee, making it more congested, and hence resulting in an increase in the interference level. It offers low-power, low cost, robust, and bi-directional communication. To add reliability to the transmission, it provides acknowledged transmission. As it has higher data transmission rates, the power consumption is more than Sigfox and LoRa [21]. Ingenu was originally designed and focused towards applications in utility,  
Figure 6: Internet of Things (IoT) mixed architecture.  
oil, and gas sectors. Nowadays, it is being proposed for a diverse range of applications such as smart city, agriculture, asset tracking, fleet management, and smart grids, among others.
## 4 Discussion
Maintenance procedures included the cleaning of the system, namely, the filtration system (weekly), the tank units (monthly) and the aeration system (in case of hose blockage, which was only observed once in a period of biofilm proliferation). The operation on continuous flow will be an improvement but it will also increase energy expenses. Therefore, measures and adaptations are under study in order to successfully upscale the designed system. As seen, the composition of the macroalgae will depend upon cultivation conditions, but also, upon growth location, seasonality and specific species [36]. With low maintenance and low control of the cultivation procedure, the proliferation of epyphites in the cultivated macroalgae is likely to occur. For that reason, the application of the biomass produced can be targeted for agriculture as fertiliser. _Ulva_ species are composed by valuable polysaccharides, including ulvans and oligosaccharides, protein and essential amino acids, macro and trace elements, e.g., iron and manganese and in lower content, lipids (e.g., n-3 polyunsaturated fatty acids (PUFAs), such as \(\alpha\)-linolenic acid, eicosapentaenoic acid and docosahexaenoicenoic acid [81; 82; 83]. With such chemical composition, _Ulva_ spp. is a valuable resource with economical interest and that can be applied for food, feed and fertiliser. The application of _Ulva_ spp. as soil amendments, either in bulk or as an extract, has proven to be beneficial for crops with increased germination, growth rates and root proliferation [84; 85; 86] and improved plant development [87; 88]. However, also, for soil, improving its structure [89]. Macroalgae-based fertilisers or biostimulants are biodegradable, non-toxic, non-polluting, and non-hazardous to humans, terrestrial and aquatic organisms. Considering that agriculture is an important economic sector of _Vale do Mondego_ area, the application of the farmed _Ulwa_ as fertiliser will contribute for a more sustainable agriculture and improve regional economy.
## 5 Proposed mMTC Device Grouping Algorithms
### Previously Proposed Algorithms
#### 5.1.2 MILP Solution with Fixed Time Constraints  
Because of the quick and ever-growing expansion of the number of mMTC devices in the IoT environment, a strict constraint-based algorithm will lose its effectiveness over time. With the same idea, we tried to gain more control over group creation by adding a packing vector \((\Gamma)\). This packing factor helped us control the number of devices per group.  
The problem was further relaxed and presented as a mixed integer linear programming problem (MILP) by converting the time constraints from applications to group points of view. We define the row vector \(\mathbf{g}_{i}\) as the \(i\)-th row of \(\mathbf{G}\), indicating devices in a group \(i\). Referring to Figure 2, from a group point of view, tasks can be scheduled to run consecutively, and the waiting time is therefore eliminated when saturated. If we replace the application-dependent time constraint with a group-specific \(\tau_{G}\), the constraint becomes fixed and linear. The relaxed group-based condition is  
\[\mathbf{g}_{i}\Bigg{(}\sum_{m\in\mathcal{M}}\mathbf{S}_{m}\Bigg{)}^{\mathsf{ T}}\mathbf{t}\leq\tau_{G}\cdot y_{i},\quad\forall i\in\mathcal{P}. \tag{23}\]  
The constraint (18) in the original problem is replaced by (23) to form an MILP problem.  
The MILP problem can be solved by mixed-integer optimizers such as MOSEK [44]. MOSEK applies the linear programming-based algorithm to solve the MILP optimization problem.  
In the previous solution, solving the MILP problem required predetermined device and group sizes. However, it is not practical to define and set a constant value for the total device time and the group size. Because of this, we used a method that solves the MILP problem after the best fit decreasing (BFD) solution. Aided by the result of the BFD model,we use the obtained values to determine the group size and the device size in the MILP solution. At the end of the process, the algorithm minimizes the total number of groups created, denoted \(\sum_{i\in\mathcal{P}}y_{i}\).
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 2 Related Work  
### YOLOv8 Network  
YOLO [3], introduced by [PERSON] et al. in CVPR 2016, revolutionized object detection with a real-time end-to-end approach. The methodology involves a two-step process, where the first step detects potential object regions, and the second step utilizes a classifier for these proposals. Unlike Fast R-CNN [14], YOLO adopts a more straightforward output approach, incorporating both probabilities for classification and box coordinates for regression in a single output, enhancing its efficiency and simplicity in comparison to the two separate outputs approach of Fast R-CNN.  
YOLOv8, a cutting-edge state-of-the-art (SOTA) model, builds upon the success of its predecessors by introducing new features and improvements. The primary objective is to enhance overall performance and flexibility. Similar to YOLOv5, the architecture consists of a backbone, head, and neck. The backbone and neck part draw inspiration from the YOLOv7 [15] ELAN [16] design. They substituted the YOLOv5 C3 structure with a C2f structure, which facilitates a more extensive gradient flow. Additionally, they finetuned the number of channels for various scale models, leading to a substantial improvement in the overall performance of the model. The head part contains significant changes compared to YOLOv5. It adopts the currently mainstream decoupled head, separating the classification and the regression tasks, and replaces anchor-based with anchor-free. In the loss part, the task-aligned assigner [17] label-matching strategy is employed, and distribution focal loss [18] is introduced. YOLOv8 employs mosaic augmentation during training but disables it for the final 10 epochs to mitigate potential detrimental effects throughout the entire training process.  
YOLOv8 offers five scaled versions: YOLOv8n (nano), YOLOv8s (small), YOLOv8m (medium), YOLOv8l (large), and YOLOv8x (extra-large). Notably, YOLOv8x, evaluated with the MS COCO dataset test-dev 2017, achieved an impressive AP of 53.9% with an image size containing a longer side of 640 pixels, surpassing YOLOv5's performance of 50.7% on the same input size. Additionally, YOLOv8x demonstrated a remarkable speed of 280 FPS on an NVIDIA A100 and TensorRT during evaluation.
## 4 COIN-LEO Algorithm
### Selecting Satellite for Collaborative Inference  
As mentioned above, the LEO satellite network adopts a mesh-like structure. The one-way propagation delay for each hop of LEO satellites is approximately 7-15 ms. As for DNN inference tasks, the inference time can vary from tens of milliseconds to several seconds, depending on the model's size. For some relatively simple inference tasks, this propagation delay cannot be ignored [25]. Therefore, our method of selecting satellites tends to reduce the propagation delay in the collaborative inference, specifically the number of satellites that data/intermediate results have to travel through.  
To achieve the desired inference delay and throughput performance, we can choose a structure in which multiple satellites are sequentially connected. By forming a circular structure in which satellites are connected end-to-end, the sum of all ISL propagation delays is reduced, as shown in Figure 5. When one satellite completes a part of an inference task, the intermediate result can be directly transmitted to the next satellite node without requiring additional transmission paths. This effectively controls the transmission delay of intermediate results and model parameters, improving the system's response speed and inference efficiency. Additionally, this circular structure can better adapt to the dynamic changes in the satellite network. When new satellite nodes join or leave the system, the topology structure can be adjusted and adapted relatively flexibly.  
We choose to connect four satellites end-to-end as our structure. This decision was made considering that as the number of satellite nodes increases, the corresponding transmission delays also increase. Considering the model size and inference speed, using too  
Figure 5: Examples of selecting satellites for collaborative inference.  
many satellite nodes may have a negative impact on throughput and other performance metrics. To strike a balance, we opt to use four satellites for distributed deep-learning inference. On one hand, the one-way propagation delay of the ISL ranges from 7 to 15 ms and the transmission delay increases linearly as the number of satellites increases. In the scenario with four satellites, the transmission delay for intermediate results to traverse each satellite and return to the initial satellite is approximately 28-60 ms, which is within an acceptable range. On the other hand, if the number of satellites is too low, it would hinder the advantages of collaborative inference and make it challenging to improve the system's throughput. If the number of satellites is even fewer than four, it would not achieve the expected benefits of collaborative inference. Considering these factors collectively, this configuration allows us to maintain a reasonable trade-off between the number of nodes and the overall performance of the system.
## 7 Simulation Results
### Results Discussion  
The reactive algorithm has proven to be an efficient mechanism to adapt the transmission to the channel changing requirements and, at the same time, save precious energy. The reactive algorithm computes the optimal transmission power level based on the PER values measured from an exhaustive characterization of each patient. This characterization is performed for every link, all postures and every power level of the radio. Therefore, any new user of the system must face up to this characterization phase, which can be a very tedious process and a time-consuming task for both the patient and the technical staff. The situation can be even more complicated for cases with patients with chronic conditions or patients with reduced mobility.  
On the other hand, the reactive algorithm has proven a similar performance and energy savings for adapting the transmission to the changing channel, but this time with the enormous benefit of avoiding the characterization phase per user. Although the building of the A-LQE model of the predictive algorithm was a very time-consuming task for us, as it required a great effort to collect all of the database used for its training, this task was carried out only once and allowed us to set up a model of path loss for a mobile wireless BAN channel. The advantage of the predictive algorithm is that it can be used in any patient without making a new or additional characterization, as our work has shown. This extensive work was finalized by upgrading the simulation capabilities of Castalia with a new path loss model that enables the proactive set up of the power transmission level.  
Both algorithms show acceptable PER values, lower than 6%, and although the energy savings obtained by the predictive strategy are slightly lower than the reactive ones, they represent more than a 22% energy savings. These results could still be improved by tuning the safety RSSI thresholds defined for the TPC block. The obtained results are of great interest in terms of battery autonomy and user satisfaction and can be implemented with low cost and penalty, as they exploit the multiple transmission power levels provided by current radio chips.
# Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms
###### Abstract
More importantly, the machine learning methods have the intelligent characteristics of automatic analysis and continuous learning, which provides effective help for this study. However, such methods generally require manual field testing of classification indices, such as rock strength and rock mass integrity, followed by manual input into the system. None of these parameters applied to the intelligent classification of surrounding rocks can achieve real-time automatic collection in the tunneling process. The emergence of measure-while-drilling (MWD) technology provides a good solution to this problem. The correlation between the drilling parameters and the surrounding rock quality parameters was studied and explored by scholars as early as the 1960s and 1970s [39; 40; 41; 42; 43]. Using field experiments with statistical analysis, the correlations between drilling parameters and surrounding rock quality parameters, such as the uniaxial compressive strength [44; 45; 46], shear strength [47], Schmidt rebound hardness [48], cutting performance (Kerf angle d and specific energy) [49; 50], RQD [51], and zones of volcanic weathering and decomposition grades [52] have been studied. In recent years, drilling parameters have been used for surrounding rock classification based on the Q method [53] or RMR method [54]. Although these previous studies have confirmed the existence of a relationship between drilling parameters and the quality of surrounding rock, the correlation based on current research of the drilling parameters and the surrounding rock quality parameters mostly refers to a certain lithology. Furthermore, the samples do not cover common rock lithology and are not universal. This is mainly because the use of an intelligent drill jumbo (which refers to a drill jumbo that can automatically collect and transmit drilling parameters) for tunnel construction is low, which makes it more difficult to collect sufficient drilling parameter samples for the surrounding rock classification. Thus, the classification method with more objective, intelligent, and efficient evaluation requires further study. The purpose of this research is to introduce 10 machine learning algorithms to predict the quality of surrounding rock using MWD data (drilling parameters) obtained from five tunnels of the Zhengzhou-Wanzhou line of the high-speed railway project in China. Through comparative analysis, three machine learning models with better comprehensive performance among them were selected to establish the tunnel surrounding rock intelligent classification system by the drill and blast method. The results of this study lay a solid foundation for the dynamic design and intelligent construction of tunnels.
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
## 2 System Model
#### Network Packet Framework
The set of packets received by the devices is named as acquired \(A\) such that \(A=\frac{p}{D}\). In the second stage, the packets not acquired by the devices are recovered jointly via the D2D-satellite link by utilizing the cooperative admission control with packets available in the network from the first stage. The network codes for multiple interfaces are matched with the joint admission preference list for mode selection for the best capacity and high SINR during the transmission of the packets. This approach increases the chance of the packets to receive successfully in the next time slot and provides effective utilization of the physical layers. The network code is beneficial to the satellite link during re-transmission as well, but in our case, the transmission of packets from D2D devices to the satellite is ignored. The satellite's purpose is to relay the missing packets, so the satellite will not receive any packets by the D2D devices. This reduces the link budget cost.
## 4 Channel State Prediction via ML Algorithms
### Employed DNN Model  
The channel state data representation introduced in Section 3 is characterized by spatial and temporal components. Convolutional neural networks have the ability to capture spatial features, while recurrent neural networks--and in particular, LSTM (long short-term memory)--can induce dependence over time. However, by simply stacking these kinds of layers, the dependence between space and time features may not be captured properly. In [37], a network structure able to capture spatio-temporal dependencies, ConvLSTM, is proposed. The ConvLSTM layer is a recurrent layer similar to LSTM, but internal matrix multiplications therein are exchanged with convolution operations. ConvLSTM can provide an effective tool for mmWave/THz channel estimations, as it has the ability to handle prediction from time series, where convolutional operations capture features while LSTMs capture time dependencies in the extracted features.  
The proposed DNN model using ConvLSTM is shown in Figure 7. It is composed of nine layers. The input of the network is a three-dimensional tensor (\(N\), \(K_{\theta}\), \(K_{\varphi}\)), where \(N\) represents the number of previous frames that the DNN uses to predict the next frames. We use \(N=4\) in our setup. The second and third dimensions, \((K_{\theta},K_{\varphi})\), represent the size of the input frame \(V_{\mathrm{X}}\). The output of the network is a tensor of the size (\(N_{\mathrm{out}}\), \(K_{\theta}\), \(K_{\varphi}\)), where \(N_{\mathrm{out}}\) is the number of predicted frames, which in our case is one.  
The first layer of the DNN is the input layer accepting \(N=4\) frames of size \(K_{\theta}\times K_{\varphi}\). The second and sixth layers are ConvLSTM with 32 filters and a convolutional kernel of size \(3\times 3\) to extract spatio-temporal features of the input data. The fourth is a convLSTM layer with 64 filters and a convolutional kernel of size \(5\times 5\) to extract more detailed spatio-temporal feature information. The activation function of the convLSTM layers is of type tanh, which helps regulate the values passed through the network. After the secondand fourth layers, there is a normalization layer that normalizes the output and provides better convergence. There is a dropout layer after the last normalization layer with a 50% probability in accordance with the recommended rates between 0.5 and 0.8 for hidden layers [38; 39]. The dropout layer is added to enhance the robustness of the network and avoid overfitting. The output layer is Conv3D with a single filter and size \(3\times 3\times 3\).  
The previous model was implemented and trained in Python using the _TensorFlow_ platform. We used the _Adam_ algorithm and updated the network parameters with learning rate 0.001 and mini-batch size of 50 samples for 50 epochs. _MSE_ was used as the loss function.