esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Experimental Result of the Relative Strength Index  
In this experiment, according to Formulas (15) and (16), three labels of prince rising (Up), prince falling (Down) and flat (Flat) are set for 20-80 ISI and 30-70 ISI, respectively. The experimental results are shown in Figures 5 and 6 and Table 1. Figure 5a demonstrates the 100 epochs versus model loss, while Figure 5b shows the data of training accuracy at 100 epochs. Table 1 and Figure 6 show the Recall, Precision, F1, average Accuracy and Confusion Matrix of three categories in the test set. During the training process, the loss of the training data decreases steadily, but the loss of the verification data converges at about 10-20 epochs, and increases when it exceeds 80 epochs, indicating that too many training epochs may cause the model to overfit. The result in Figure 6a shows that in 20_80_RSI, most of the 'Flat' category can be correctly predicted, but a certain proportion of 'Up' and 'Down' categories will be misjudged as 'Flat'. It can be clearly seen from the experimental result in Figure 6b that, compared with 20_80_RSI, 30_70_RSI has a slightly lower proportion of false positives for the 'Up' and 'Down' categories. The experimental results show that the estimated accuracy of the 20-80 ISI in three categories is 79.2%, which is higher than 76.4% of the 30-70 ISI. In the data of Recall, Precision and F1 measure, 20-80 ISI is also slightly better than 30-70 ISI. Among them, Precision and Recall in the 'Flat' category reach up to 91.3% and 83.2%. In the three categories of predictions, the 'Flat' prediction performs best, and the 'Up' and 'Down' prediction has a phenomenon of misjudgment of each other in few cases. In addition, the Recall of the proposed model is between 64.2% and 83.2%, and the average is 71.66%, which means that more than 70% of the turning signals can be correctly estimated.  
Figure 5: (a) Loss vs. 100-epochs and (b) Accuracy vs. 100-epochs of 20_80_RSI-labeled training data.
###### Abstract  
The 3D image segmentation is the process of partitioning a digital 3D volumes into multiple segments. This paper presents a fully automatic method for high resolution 3D volumetric segmentation of medical image data using modern supervised deep learning approach. We introduce 3D Dense-U-Net neural network architecture implementing densely connected layers. It has been optimized for graphic process unit accelerated high resolution image processing on currently available hardware (Nvidia GTX 1080 ti). The method has been evaluated on MRI brain 3D volumetric dataset and CT thoracic scan dataset for spine segmentation. In contrast with many previous methods, our approach is capable of precise segmentation of the input image data in the original resolution, without any pre-processing of the input image. It can process image data in 3D and has achieved accuracy of 99.72% on MRI brain dataset, which outperformed results achieved by human expert. On lumbar and thoracic vertebrae CT dataset it has achieved the accuracy of 99.80%. The architecture proposed in this paper can also be easily applied to any task already using U-Net network as a segmentation algorithm to enhance its results. Complete source code was released online under open-source license.  
3D segmentation; brain; deep learning; neural network; open-source; semantic segmentation; spine; u-net +
Footnote †: journal: Applied Sciences
## 1 Introduction
The findings indicated that the geosynthetic encasement of the GECs caused higher shear stresses to be mobilized within the granular column, and the magnitude of these shear stresses increased with an increase in normal pressure. Additionally, the study found that the tensile forces in the geosynthetic encasement were mobilized in both the circumferential and vertical directions, providing additional confinement to the granular column. Overall, the study suggests that GECs can be an effective way to reinforce slopes and improve their stability and bearing capacity. It is worth mentioning that, the effectiveness of GECs can depend on a variety of factors, including the soil conditions, the size and spacing of the columns, and the load conditions. It is also important to consider the costs and other practical considerations when deciding whether to use GECs in a particular project. [PERSON] and [PERSON] [20] conducted a numerical analysis to understand the behavior of inserting OGC and GEC on the embankment's stability constructed on soft clay, it was concluded that GECs contributes to increase the safety factor and the overall stability of the system. Analyzing the soil-column interaction at the slip\(-\)surface can help engineers to understand how the soil and column behave under lateral load and to design the structure appropriately, there have been a number of experimental and numerical investigations into the performance of geosynthetic encased granular columns under both horizontal and vertical loads. These studies have consistently shown that geosynthetic encased granular columns enhanced lateral load resistance and reduced lateral displacement compared to ordinary granular columns, as well as improved shear strength [21; 22; 23; 24; 25]. The failure mode of the columns was reported in different patterns; ordinary and encased granular columns may fail in different modes under combined horizontal and vertical loads, with failure at the center line of the embankment due to bulging and failure at the toe of the embankment due to bending or shear [9; 22; 26]. Researchers have devoted a significant amount of attention to the use of geosynthetic granular columns to strengthen the soil beneath embankments. However, the issue of shallow slope failure has not received as much attention. The current study aims to address this by considering slope protection for both deep-seated and shallow failure in order to enhance the stability of the embankment overall. In order to achieve the mentioned goal, two models were employed; the first model aimed to determine the normalized shear stress of the soil and the normalized lateral displacement of the columns resulting from soil movement along the shear plane. Additionally, a slope model was simulated to analyze the impact of various factors, including the stiffness ratio between the geogrid and the soil, area replacement ratio of the granular columns, and the angle of the embankment slope on the values of factor of safety. It is widely acknowledged that the stability of embankments immediately after construction is crucial, thus short-term analysis were considered in this analysis.
## 2 Related Work
### Hybrid Optimization Frameworks  
Hybrid methods mix global search and local optimization. MAHACO [28] merges ACO with stochastic approaches to 3D planning, and AQLPSO [29] fuses Q-learning with PSO for diversity. PSO-DQN [30] merges swarm intelligence with DQN for smoothness. The improved nutcracker optimization algorithm (INOA) [10] achieves exploration-exploitation balance through chaos mapping, and the multi-algorithm hybrid ant colony optimizer (MAHACO) [28] enhances 3D path smoothness. Another hybrid approach [31] integrates LLMs with telemetry and scenario data to create adaptive mission commands. It combines retrieval and generation to adjust decisions based on real-time conditions. However, these methods do not use GAs' population diversity and RL's targeted exploitation at mutation phases--a gap our method bridges.
# Shell Waste Management and Utilization: Mitigating Organic Pollution and Enhancing Sustainability
## 2 Cleaning Shells  
Within the meaning of Regulation (EU) No 1069/2009, shells with soft tissue and flesh completely removed are not considered as by-products, but as waste. For some reutilization purposes, such waste shells need to be cleaned prior to their industrial use. Particular applications call for specific ways of pretreating and cleaning. Namely, for using bivalve shells as a source of CaCO\({}_{3}\), any residual organic material would decrease the purity of the CaCO\({}_{3}\)[8; 22; 23]. Shells with organic residue attached can be washed with large amounts of freshwater to remove salt, and after that heated at 500 \({}^{\circ}\)C to burn off the residual organic material [24]. The more energy efficient and environmentally friendly method for shell cleaning is using the enzymatic cleaning, as enzymes can digest proteins at temperatures close to ambient and may also produce an additional product stream, such as protein hydrolysate [8]. Enzymatic hydrolysis can also be applied for cleaning of shrimp shells and bio-conversing them into valuable nitrogen-containing compounds, although intact shells are poorly hydrolyzed by chitinases unless pre-demineralization and deproteinization with chemicals or proteases is conducted [25]. To facilitate the hydrolysis of crayfish shell into oligosaccharides, ball milling and ultrasound-assisted hydrogen peroxide decolorization is applied for pretreating [26].  
For ammonia nitrogen removal in a biological nitrifying system using oyster shells as alkalinity-releasing filling materials, shells can be cleaned by ultrasound in water for 20 min, and then dried at room temperature [27]. To remove the attached impurities from mollusc shells, in order to synthesize aragonite needles by carbonation, shells are cleaned with water and ethyl alcohol [28]. For the purpose of catalyst preparation, crab shells can be manually cleaned to remove protein and other organic material, and washed with warm water before drying at 105 \({}^{\circ}\)C [29].  
On a smaller scale, shells of gastropods are sometimes buried for a certain period, so that an internal cleaning process can take place through decomposition by detritivore organisms in the soil [19]. Cleaning of residual organic material from a smaller quantity of shells can also be accomplished by brushing, boiling or sun-drying [30; 31].
## 6 What Needs to Be Done?
### Improve Dosimetry in Biological Studies
#### 6.2.1 Assessing Environmental Exposures from MIMO Antennas  
Exposure assessment for transmitters employing MIMO antennas is more complex than for conventional antennas, whose beams are fixed in space. One approach is to use statistical methods to calculate distributions of exposure, and this is currently being studied by IEC/IEEE ad hoc committees, as well as equipment manufacturers. However, statistical exposure assessment is likely to employ proprietary software and will be inherently less transparent than exposure calculations for fixed-beam antennas, for which beam patterns are publicly available and calculation methods are specified by the FCC.
## 2 Transcriptional Regulation in Integrons
### Transcription of Gene Cassettes Independent of Pc and Cassette-Borne Promoters
#### 2.4.2 Group II Intron Elements  
Another example of mobile genetic elements contributing to promoterless gene cassette expression is Group II intron elements [90]. Group II introns are mobile genetic elements pointed as the ancestors of spliceosomal introns and retrotransposons in eukaryotes [91]. Among several Group II intron types, the Group IIC introns are featured by invading intergenic regions usually after inverted repeat sequences, such as _attC_ sites, and in this case, they are named group IIC-_attC_ introns [92]. The _S.ma._I2 Group IIC-_attC_ intron was found splitting the first resistance gene from its cognate _attC_ site in an integer of _S. marcescens_[90]. [PERSON] et al. [93], revealed that this _S.ma._I2 insertion disrupted the transcription of downstream gene cassettes from the integron Pc promoter (PcW + P2 configuration), probably due to the increase in the physical distance between cassette and Pc. However, in silico and in vitro analyses revealed that _S.ma._I2 insertion supplied a functional internal outward-oriented promoter (P\({}_{\text{out}}\)) that was active in expressing promoterless gene cassettes that had been silenced the insertion of the intron (Figure 5). Moreover, other P\({}_{\text{out}}\) promoter versions sharing sequence similarity with the _E. coli_ promoters were conserved among group IIC-_attC_ intron sequences. These promoters presented different transcription strengths that ranged between those observed for PcS and PcW from class 1 integrins [93].
## 2 Materials and Methods
### Data Collection
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Test Type** & **Assessment Modality** & **Description** & **Tool/Device** & **Application Area** & **Purpose** \\ \hline \multirow{3}{*}{QualST} & \multirow{2}{*}{Light touch detection} & \multirow{2}{*}{Detection of gentle tactile input} & \multirow{2}{*}{Cotton swab} & Symptom area + contralateral control & Screening for hypoesthesia \\  & & & & & \\ \multirow{3}{*}{Pressure sensitivity} & \multirow{2}{*}{Response to blunt mechanical input} & \multirow{2}{*}{Tongue depressor or spatula} & \multirow{2}{*}{Symptom area} & Evaluation of pressure perception \\  & & & & & Assessment of mechanical mechanical \\ \multirow{3}{*}{QST} & \multirow{2}{*}{Light touch detection} & \multirow{2}{*}{Distinction between sharp and dull stimuli} & \multirow{2}{*}{Dental explorer or toothpick} & \multirow{2}{*}{Symptom area} & \multirow{2}{*}{Screening for thermal hypoesthesia} \\  & & & & & \\ \multirow{3}{*}{Thermal response} & \multirow{2}{*}{Themal response} & \multirow{2}{*}{Subjective reaction to cold stimuli} & \multirow{2}{*}{Ice stick} & \multirow{2}{*}{Symptom area} & \multirow{2}{*}{Symptom area} \\  & & & & & \\ \hline \multirow{3}{*}{QST} & \multirow{3}{*}{Current perception threshold (CPT)} & \multirow{3}{*}{Elelectrical stimulation at 2000, 250, and 5 Hz to assess A\(\beta\), A\(\delta\), and C fibers} & \multirow{3}{*}{NeuroMeter C fibers} & \multirow{3}{*}{Facial skin (Mx., Mn. divisions)} & Quantitative evaluation of sensory nerve function \\  & & & & & \\ \multirow{3}{*}{Two-point discrimination} & \multirow{3}{*}{The minimum distance between two distinct points} & \multirow{3}{*}{Caliper or blunt compass} & \multirow{3}{*}{Tongue} & Spatial resolution and fine touch discrimination \\ \hline \end{tabular}
\end{table} 
Table 1: Sensory testing protocol: qualitative and quantitative assessments performed at initial evaluation. Sensory function was assessed using a VAS at both the initial and final clinical visits. In this study, a VAS ratio of 1.0 was defined as normal sensation. Ratios below 1.0 indicated hypoesthesia, whereas values above 1.0 were interpreted as hypeesthesia or allodynia. Negative values were not permitted, as ratios represent relative intensity and cannot be negative by definition. Treatment-related variables included whether the causative factor (e.g., implant or prosthesis) was removed, the treatment modality applied (pharmacological, physical therapy, surgical, or no treatment), and the total duration of treatment, recorded in months.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 2 Related Work
### The Immersive Video Use Case  
High-resolution video processing and Content Delivery Networks (CDNs) can greatly benefit from MEC and NFV. References [5; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] summarize some relevant results in this field. In particular, Reference [13] shows the improvement in QoE perceived by users when CDNs are extended to the edge through the use of NFV and cloud technologies. Conversely, Reference [14] proposes the concept of CDN as a service, focusing on the optimized placement of Virtual Machines hosting caches, transcoders, and streamers, to minimize costs and maximize streaming QoE.  
To emphasize the benefits of an MEC in an NFV platform compared to a traditional video distribution service, Reference [15] exploits the users' QoE and bandwidth as performance indexes. Reference [16] considers the concept of on-the-fly-transcoding and proposes a framework in which the network edge plays a leading role in customizing the video format, based on user's expectations.  
The advantages of a cloudlet network in a video streaming application are discussed in References [17; 18].  
Reference [5] provides a state-of-the-art survey on research efforts in MEC, pointing out challenges, open issues, and opportunities. In this regard, Reference [19] defines the differences between an MEC and a Cloud Radio Access Network (RAN) approach, demonstrating that they are not exclusive technologies; rather, they complete each other and can work together in a heterogeneous mix.  
Figure 3: ETSI MEC reference architecture in an NFV environment.  
The subject of real-time video transcoding in a distributed heterogeneous environment is addressed in Reference [20]. The performance of the proposed system scales as the number of nodes increases, up to the point that the scheduler for video frames distribution becomes a bottleneck.  
Previous works carried out by some of the authors, such as References [21; 22], are related to hardware and software acceleration at the network edge, and GPU utilization in NFV-only environments; Reference [23] discusses the use of GPUs to accelerate the VP8 Video encoder, while Reference [24] introduces immersive video services.  
All the discussed works focus on traditional video networking applications, in which consumers receive the selected media contents from a central or distributed delivery system. This paper goes beyond such results since it addresses services in which users can originate and share high-quality media contents (also in real-time), by using a specific ME app (i-EVS) on top of an integrated MEC in an NFV environment equipped with High-Performance Computing (GPUs), and a related end-user app which runs on the end users' devices.
# Simulation and Design of Three 5G Antennas
## 4 Metal Frame Mobile Terminal Dual-Element \"Loop-Groove\" MIMO Antenna Design
### The Decoupling Principle of the \"Ring-Slot\" Structure
EC and AC represent the products of co-polar mode weighting coefficients for the ring antenna and slot antenna, respectively, as well as the products of cross-polar mode weighting coefficients. From the result on the right side of Equation (27), it can be observed that, if the difference between EC and AC is smaller, the resulting envelope correlation coefficient (ECC) will be smaller. Therefore, by providing reasonable excitation to the ring antenna and slot antenna, in order to achieve similar respective co-polar and cross-polar modes, the requirement of achieving a low ECC value can be met. Figure 38 shows the current distribution diagram for the dual-element \"ring-slot\" MIMO antenna structure. From Figure 38, it can be observed that, when the ring antenna is excited, the current predominantly radiates along the long side of the dielectric substrate, resulting in relatively weaker current flow through the slot antenna, which mainly circulates around the rectangular gap on the ground plane along the y-axis direction. Conversely, when the slot antenna is excited, the current radiates vertically to the long side of the dielectric substrate, accompanied by reverse currents around the rectangular gap. Therefore, in the dual-element \"ring-slot\" MIMO antenna structure, the ring antenna and slot antenna exhibit orthogonal current patterns, effectively suppressing coupling between the two antenna elements. Even without the addition of external decoupling structures, the ring antenna element and the slot antenna element achieve a high level of isolation. Furthermore, when compared with traditional antennas, the proposed antenna structure features a complete overlap of both antenna elements and, after optimization, the \"ring-slot\" structure's length has been significantly reduced to 31 mm, contributing to the miniaturization of smartphones.
# Federated Learning for Secure In-Vehicle Communication
## 2 Backgrounds and Preliminaries
### Federated Learning for Intrusion Detection in Vehicular Networks  
Google introduced federated learning, a decentralized machine learning approach that minimizes data leakage and enhances privacy [27]. The training process is based on two important steps, a set of FL clients that train a ML model using local data and a FL server that retrieves their local weights, aggregate these weights and sends the updated value to the clients for an additional training round. Federated learning allows the creation of models using datasets distributed across many devices without requiring centralized data aggregation to reduce data leakage. This approach promotes robust collaborative model training and ensures data privacy [29]. FL operates under two primary architectures: Vertical FL (VFL) and Horizontal FL (HFL). VFL combines locally trained sub-models from clients with vertically split data to build a global model [30]. In contrast, HFL involves multiple clients collaboratively training a model through a central server while keeping the training data on devices, thereby protecting privacy [30]. HFL faces challenges like non-uniform data distribution among clients [31].  
Some authors propose FL-based IDS for V2X communication to enhance security and threat detection in distributed environments. The authors [32] suggest an FL-based IDS for 5G and Beyond V2X networks to detect zero-day attacks. The method employs the Convolutional Neural Network (CNN) as the local model to identify intrusions. The authors in [33] propose FL-based IVN-IDS based on edge computing for C-V2X networks using a feedforward neural network (FNN) as a local model. The approach detects multiple types of attack according to its CICToRC-2021 dataset [34]. The FNN model can effectively classify intrusions with more than 96%, it has the drawbacks of a high memory requirement and computation time and is therefore less efficient to deploy on large scales. In addition, the lack of time perception in FNNs hinders the ability of the models to learn sequential patterns of attacks that are common in IVN traffic. In [35] an FL-based IDS using a convolutional LSTM (ConvLSTM) model is proposed to detect intrusions in IVNs. The work is edge computing-oriented, where the model is trained on CAV and the federated models are aggregated by edge servers.  
Unlike previous FL-based IVN-IDS studies [32; 33; 34; 35] that assume a single dataset divided among clients, our approach leverages three real-world CAN bus datasets (Hyundai, Chevrolet, and Kia) and assigns each dataset to a separate client. In each federated round, each client is presented with a distinct partition of its own dataset, guaranteeing strict data locality and privacy. This setting more closely resembles real-world deployment in networked vehicle environments, where centralized data collection becomes unrealistic. Through decentralized, incremental learning over data inherently, our solution finds competitive performance with privacy ensured in a realistic federated environment.