esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 MASLD-HCC Treatment
#### Immunotherapy Outcomes in MASLD-HCC Patients
Notably, anti-VEGF therapy has been shown to counteract this immunosuppression through multiple mechanisms, including reducing Treg percentages, enhancing CD4+ and CD8+ T cell infiltration into the tumor, decreasing PD-1 expression on intratumoral CD8+ T cells, and ultimately promoting tumor destruction. Anti-VEGF agents can improve intratumoral infiltration and survival of CTLs by normalizing the vasculature and modulating the immune microenvironment, thus enhancing the response of HCC to ICIs. Recent studies have also highlighted VEGF's involvement in TOX-dependent T cell exhaustion [131], reinforcing the importance of combining anti-VEGF therapies with anti-PD-1/PD-L1 therapies. As discussed in previous sections, beyond PD-1, PD-L1, and CTLA-4, other immune checkpoint molecules, such as TIM-3 and LAG-3, can also be targeted to stimulate an anti-tumor immune response. To provide a comprehensive overview of the available therapeutic options, we will briefly discuss immunotherapies targeting these molecules. However, it is important to note that research on these targets remains limited, and further human trials are required. These immunotherapies are not yet included in official guidelines and are not widely used in clinical practice. Targeting TIM-3 has demonstrated anti-tumor efficacy in preclinical studies. Combining TIM-3 inhibitors with other immune checkpoint inhibitors, chemotherapy, or radiotherapy has shown synergistic anti-tumor effects in tumor mouse models. However, additional human trials are necessary to confirm these findings [132]. Similarly, anti-LAG-3 immunotherapy has shown promise in this field. LAG-3-based therapies can be classified into three categories: anti-LAG-3 monoclonal antibodies, bispecific LAG-3 antibodies, and LAG-3 immunoglobulin fusion proteins [133]. However, emerging evidence suggests that targeting LAG-3 alone may not be the most effective therapeutic strategy, as tumor cells can evade anti-tumor immune responses through various molecular mechanisms [134]. A recent study in HCC patients demonstrated a correlation between pre-treatment expression levels of LAG-3, CD8, STAT1, and LAG-3+CD8+ cells in tumor tissue and response rates to immunotherapy in individuals with advanced HCC. Notably, LAG-3+CD8+ cells emerged as the most favorable biomarker. The study found that immunohistochemical analysis of LAG-3 and CD8 expression (LAG-3+CD8+ phenotype) was useful in predicting responses to ICIs in treatment-naive patients with advanced HCC [135]. Interestingly, only LAG-3+CD8+ cells showed a statistically significant correlation with response to ICIs, regardless of whether the HCC was viral- or non-viral-related. These findings suggest that pre-treatment immunohistochemical assessment of LAG-3 and CD8 levels in the tumor microenvironment may serve as a predictive biomarker for response to ICIs in HCC patients.
## 3 Experimental Verification
### Experimental Results  
The 40 captured images were input into the computer, and the angles \(\theta\) between the spring axis and the reference axis were calculated using the algorithm proposed in this paper. The results are shown in Table 1. From the detection results, it can be seen that the angles from the 1 st to the 24 th image show an increasing trend, with the angle being maximum for the 25 th image. The angles from the 26 th to the 40 th image show a decreasing trend. The variation in angle conforms to objective rules, further proving the feasibility and correctness of the detection method proposed in this paper.  
Taking the maximum angle of 1.0254\({}^{\circ}\) from the 40 images as the maximum variation of the spring relative to the reference axis, the coaxiality error \(\Delta_{\mathrm{x}}\) could be calculated using Equation (1). The calculated result met the requirements of coaxiality tolerance. The delivery inspection report of the measured part is a qualified product, which proves the feasibility of the detection method in engineering practice, and the coaxiality evaluation schematic is shown in Figure 16.  
Figure 15: Detection system.  
To further validate the stability of the algorithm proposed in this paper, we conducted coaxiality testing on ten automotive brake piston components. Among them, seven were qualified products and three were unqualified. The results of the testing using the algorithm proposed in this paper were consistent with the delivery inspection reports of the measured parts. A total of 400 images were obtained for the ten automotive brake piston components, with three images failing to extract geometric feature points, resulting in a success rate of 99.25%. This further demonstrates that the detection method proposed in this paper meets the online detection requirements for automobile brake piston components.
# Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels
## EDM Process Details
### Various EDM Process Variants  
#### 2.4.1 Sinking EDM or Sinker Type EDM  
The sinker type EDM set-up is schematically shown in Figure 2. In the sinking type EDM process, two machining systems are applied. In the first system, a controlled electrical spark is repeated until the electrode shape is replicated over the workpiece surface. In the second system, the electrode moves in three-dimensional (3D) space to machine a complex 3D shape on work surface. A hybrid of the two cutting systems, however, is possible but remains to be reported. The sinking EDM process has a characteristic sharp rise in the temperature (i.e., 8000 to 12,000 \({}^{\circ}\)C) of the machining area. EDM machines have a unit control system that controls and monitors the machining variables and shows the process execution sequence. A wide range of electrode materials are used, the choice of which also depends on the workpiece materials; copper and copper-doped metals with graphite, among others, are widely used as the electrode materials in the sinking EDM process. Due to electrode wear, the electrode is often re-shaped to execute the finishing operations. Normally, this process uses a hydrocarbon dielectric fluid because of its positive effect on the SR and electrode wear rate (EWR).  
Figure 2: Schematic diagram of sinking EDM.
## 4 Experimental Evaluation
### Experimental Results and Analysis
#### 4.4.1 Ablation Experiment  
Ablation experiments were carried out to assess the impact of different modifications on the original network, ensuring consistency in the experimental environment for accuracy and performance. The results, which are presented in Table 2, showcase the effectiveness of these modifications. Improved models 1-3 integrate SPPEPSA, GSEAM, and GSCConv individually into the YOLOv8s network, while improved models 4-6 combine two of these enhancements in the original network.  
Table 2 indicates that incorporating the SPPEPSA module into the YOLOv8 backbone network boosted mAP50 by 0.38% and mAP50-95 by 1.05%, mainly due to replacing the SPPF module. Despite a slight increase in Params and FLOPs, this adjustment enhanced the model's multi-scale detection ability.  
Replacing the C2f setup with GSEAM in the YOLOv8s neck network reduced Params and FLOPs by 1M and 1.9G, respectively, while improving mAP50 by 0.46% and mAP50-95 by 1.53%. This enhancement better captured feature relationships for more accurate bounding boxes.  
Integrating GSCConv into the neck network moderately raised mAP50-95 by 1.55%, with negligible changes in Params and FLOPs. Combining all three enhancements led to a 0.99% increase in mAP50, a substantial 2.67% rise in mAP50-95, almost no change in Params, and a 1.0G FLOP reduction compared to the base YOLOv8s network.  
This study demonstrates that the proposed network enhances feature extraction, aiding in capturing intricate details in complex and low-light settings. This optimization notably benefits high-level IoU thresholds, thereby enhancing model performance in indoor scenarios. The improved YOLOv8s exhibit superior human detection efficiency in challenging environments without increasing model complexity while also reducing FLOPs.
## 3 Results
### Recognition of Tissue Structures Using Individual KMeans Clustering and PLS-DA of Spectral Images  
On each image of the calibration samples, spectral processing by individual Kmeans clustering was applied for recognition of the tissue architecture. The selection of clusters (or groups of pixels/spectra) of interest is a step necessary for the subsequent construction of the PLS model. This Kmeans treatment permits the identification of different histological structures. Based on their spectral signatures, we report in Figure 1A,B (R sample) and Figure 1E,F (NR sample) that relevant histological structures were identified, such as an invasive tumor, connective tissue, or muscle. The color-coded Kmeans images were constructed using an advanced version of the clustering that determines automatically the number of the clusters [26]. This algorithm permits a fine description of the tissue architecture. However, here the objective is to associate characteristic spectral signatures to the main tissue structures constituting the TURBT samples. So, from the color-coded Kmeans, only the majority clusters were considered for further analysis. For each image, keeping the 6 majority clusters appeared appropriate to retain the large majority of the tissue structures of interest. Note also that minor clusters can be considered as outliers since their constituting pixels are very weakly representative (representing a proportion of pixels less than 1%) and very often localized at the tissue edges. Then, from the Kmeans selected clusters, a PLS-DA model was constructed for the automatic identification of pixels of interest in the predictive approach implemented here. The efficiency of this model can be first evaluated on the same samples of the calibration set, as shown in Figure 1C,G. Pixels colored in black corresponded to non-selected pixels. Orange pixels were identified using PLS-DA for subsequent scoring on the R/NR scale. The strong correspondence between these orange pixels and the majority of Kmeans clusters can be noted for these calibration samples, as it can be noticed by visual comparison of Figure 2B,C (R sample) or Figure 2F,G (NR Sample).  
Figure 1: Histological images (**A**,**E**) and chemometric steps; including individual Kmeans clustering (**B**,**F**) for tissue structures recovering; PLS-DA (**C**,**G**) for automatic selection of pixels of interest and R/NR PLS scoring (**D**,**H**); for two representative calibration samples corresponding to NR and R patients. PLS was run with the infrared images of TMA spots allocated to calibration set as reference inputs; by considering a scale from 0 to 1 with scores of 1 (blue) and 9 (red) for NR and R respectively.
# Adaptive Emergency Call Service for Disaster Management
## 4 Adaptive Multi-Hop Emergency Call Protocol: 5G-SOS
### 5G-SOS Adaptive Parameters
#### 4.1.4 Emergency Call List  
The 5G-SOS protocol-enabled device uses two kinds of lists to store the history of received emergency calls in the buffer. In the \(\chi\) list, the device stores the identifiers of received calls represented by the couple \((srcID,callID)\), while the data of the emergency are not stored. To prevent the excessive increase of this list, the emergency calls are removed from the list after a given period (30 min). In the second list, \(\eta\), the device stores the pending received emergency calls. A call is removed from \(\eta\) when the \(T_{r}\) expires and the call is relayed by the device, or when the call is sufficiently relayed by neighboring devices. The size of \(\eta\) is limited to three calls. If the limit size is reached, new emergency calls are ignored as presented in Algorithm 2. The use of \(\eta\) is a new feature of the 5G-SOS protocol. In the M-HELP protocol, only one emergency call is processed by the device at a given time. All emergency calls received during the transmission of a previous emergency request or during the pending period before relaying a previous emergency call are ignored.  
In order to investigate the performance of our protocol, we evaluated and compared 5G-SOS with the existing M-HELP [9] and FINDER [10] protocols under extreme emergency scenarios. Thereby, the 5G-SOS testing environment, evaluation parameters, and performance analysis are presented in the next section.
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 3 Materials and Methods
### Analysis  
As a secondary methodological step, a literature review was conducted, with a focus on the open-access scientific literature available in specialized databases such as IEEE, Scopus, Science Direct, Springer, ACM, and Web of Science. At this stage of the research, search strings composed of key terms such as 'agriculture', 'agriculture 5.0','smart agriculture', '6G', 'IoT', and 'Colombia' were utilized. The selected documents correspond to the period between 2020 and 2025.  
Initially, a range of five years was considered for the analysis of scientific publications. However, greater relevance was assigned to papers published from 2023 onwards. This decision was informed by the outcomes of the World Radiocommunication Conference 2023 (WRC-23), where pivotal elements concerning the utilization of spectrum for 6G networks were established.  
The articles included in the analysis were filtered according to their relevance to the following criteria: The subjects to be addressed in this study are as follows: firstly, future challenges for the adoption of 6G networks in Colombia; secondly, projected economic applications for this technology in the country; thirdly, the current diagnosis of the Colombian agricultural sector; and fourthly, current challenges in the transition towards Agriculture 4.0 and 5.0 models.  
The in-depth analysis focused on three main areas. Firstly, the study evaluated the present status and future projections of 6G networks on a global scale, with a particular focus on their potential integration into the agricultural sector. Secondly, it analyzed the challenges and opportunities of digitally transforming the Colombian agricultural sector using Agriculture 4.0 and 5.0 technologies. Thirdly, it reviewed public policies and regulatory frameworks concerning the 6G spectrum in Colombia, with the aim of identifying barriers to, and solutions for, the effective transition to these technologies in the country's rural and agricultural context.
# A Decentralized, Flat-Structured Control System for Chiller Plants +
## 4 Case Study Based on New System
### System Description  
The aquatics centre adopts the chiller plant system for centralized cooling, with a cooling area of about 80,000 square meters. The chiller plant system mainly includes three centrifugal chillers, one centrifugal chiller with heat recovery, six cooling towers, five condenser water pumps, five chilled water pumps. These devices are mainly installed on the underground second floor. The nominal information of the main components in the cooling system is listed in Table 2. The schematic drawing of the cooling system is shown in Figure 6.  
The original automatic control system of the cooling station cannot play the role of automatic control and energy-saving operation. There are several outstanding problems: firstly, the automatic control system has been modified many times, the original system is difficult to be closed and upgraded, and the retrofitting cost is high; secondly, the automatic control system is basically abandoned in maintenance, and cannot truly realize automatic control. The number of running equipment and frequency conversion operation require manual operation, which does not reduce the labor cost and optimize the operation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equipment** & **Quantity** & **Nominal Equipment Information** & **Unit** & **Value** \\ \hline \multirow{4}{*}{Centrifugal Chiller} & \multirow{4}{*}{3} & Cooling Capacity & kW & 2813 \\ \cline{3-4}  & & Power & kW & 535 \\ \cline{3-4}  & & Design COP & — & 5.26 \\ \hline \multirow{4}{*}{Heat Recovery Centrifugal Chiller} & \multirow{4}{*}{1} & Cooling Capacity & kW & 1406 \\ \cline{3-4}  & & Power & kW & 286 \\ \cline{3-4}  & & Design COP & — & 4.92 \\ \hline \multirow{4}{*}{Cooling Tower} & \multirow{4}{*}{6} & Power & kW & 22 \\ \cline{3-4}  & & Flowrate & m\({}^{3}\)/h & 700 \\ \hline \multirow{4}{*}{Condenser Water Pump (CWP)} & \multirow{4}{*}{2} & Power & kW & 55 \\ \cline{3-4}  & & Power & kW & 365 \\ \cline{3-4}  & & Head & m & 36.5 \\ \cline{3-4}  & & Power & kW & 37 \\ \cline{3-4}  & & Flowrate & m\({}^{3}\)/h & 181 \\ \cline{3-4}  & & Head & m & 35.4 \\ \hline \multirow{4}{*}{Chilled Water Pump (CHWP)} & \multirow{4}{*}{3} & Power & kW & 110 \\ \cline{3-4}  & & Flowrate & m\({}^{3}\)/h & 637 \\ \cline{3-4}  & & Head & m & 38.2 \\ \cline{3-4}  & & Power & kW & 55 \\ \cline{3-4}  & & Flowrate & m\({}^{3}\)/h & 312 \\ \cline{3-4}  & & Head & m & 43.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Nominal information of the main components in the cooling system.
###### Abstract
In addition, current research is generally dominated by large models with many parameters and significant computation, which are not friendly enough for the equipment; hence, it is especially important to develop an algorithm that effectively improves the precision and recall under changes in parameter numbers and model size. In order to solve these problems, we propose an improved ship target detection algorithm based on YOLOv5. The main contributions of the proposed algorithm are as follows:  
(1) Aiming at the problems of semantic information loss and small sensory fields, the RFE model is introduced in the backbone part, which increases the sensory field, reduces the model parameters, and also reduces the risk of potential overfitting. The module is divided into three branches, each with two standard convolutions, as well as one null convolution. A residual block is also introduced, which significantly enhances the detection capability for large ships. (2) In order to improve the accuracy of ship type identification and the recall of ships, coordinate attention mechanism is embedded into the YOLOv5 structure. The coordinate attention mechanism considers a more efficient way to capture position and channel information, resulting in better performance of YOLOv5 in the face of overlapping targets and small targets. At the same time, the coordinate attention mechanism improves the recall of the model, which gives the improved YOLOv5 a greater advantage in marine surveillance. Coordinate attention mechanism is introduced to lift the model's performance and solve the problem of insufficient detection ability. (3) A lightweight convolutional GSconv is used in the neck section to reduce the complexity of the model and maintain accuracy, resulting in a better balance between model accuracy and speed. Similarly, the one-time aggregation module VoV-GSCSP is used instead of the C3 module to form a slim-neck module with GSConv, which serves to maintain sufficient accuracy with little change in the number of parameters. (4) Finally, in terms of the loss function, SIoU is used to replace the CIoU of YOLOv5. SIoU is used instead of CIoU to redefine the loss function utilizing the vector angle between the bounding box regressions, which effectively improves the detection accuracy and increases the convergence speed of the network. The other sections in this study are organized as follows. In Section 2, we detail the ship detection method used in this study. In Section 3, the setting and evaluation indicators for this experiment are described. In Section 4, the experimental results are presented. Finally, Section 5 provides the conclusion of this study.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## 2 Model Formulation
### The MISO Fade Channel with Noisy Feedback
Definition 6: The two-dimensional modulo-lattice function [31] (modulo-\(\Lambda\) function) is defined as  
\[\mathbb{M}_{\Lambda}[\mathbf{X}]\stackrel{{\mathrm{def}}}{{=}}\mathbf{X }-\mathbb{Q}_{\Lambda}[\mathbf{X}], \tag{7}\]  
where \(\Lambda=\Lambda_{R}+j\Lambda_{I}\) is a complex plane with \(\Lambda\in[\pm\frac{d}{2},\pm j\frac{d}{2})\), \(d>0\), \(j\) is the imaginary unit, \(\mathbf{X}\) is a complex-valued number, and \(\mathbb{Q}_{\Lambda}[\mathbf{X}]\) is the nearest neighbor quantization of \(\mathbf{X}\) with respect to \(\Lambda\). Proposition 2 ([31]):
* The distributive law \(\mathbb{M}_{\Lambda}[\mathbb{M}_{\Lambda}[\mathbf{X}]+\mathbf{Y}]=\mathbb{M}_{\Lambda }[\mathbf{X}+\mathbf{Y}]\). * If \(\mathbf{X}+\mathbf{Y}\in\Lambda\), \(\mathbb{M}_{\Lambda}[\mathbf{X}+\mathbf{Y}]=\mathbf{X}+\mathbf{Y}\); otherwise, a modulo-aliasing error has occurred. * Let the dither signal \(\mathbf{V}\) be uniform over \(\Lambda\); then, \(\mathbb{M}_{\Lambda}[\mathbf{X}+\mathbf{V}]\) is uniform over \(\Lambda\), and \(\mathrm{Var}(\mathbb{M}_{\Lambda}[\mathbf{X}+\mathbf{V}])=d^{2}/12+d^{2}/12=d^{2}/6\). * For any random variable \(\mathbf{X}\) that is statistically independent of the dither signal \(\mathbf{V}\), \(\mathbb{M}_{\Lambda}[\mathbf{X}+\mathbf{V}]\) is statistically independent of \(\mathbf{X}\).
###### Abstract  
This paper carried out wind tunnel tests and numerical analysis to study the effect of the spatial position of deflector plates on the vortex-induced vibration (VIV) of a double-deck truss girder. The wind tunnel tests found that setting the web deflector plate and the lower chord deflector plate significantly suppressed the VIV. In order to study the suppression mechanism of the deflector plate on VIV, numerical simulations were conducted using the computational fluid dynamics (CFD) method. We analyzed the suppression mechanism of the deflector plate on VIV by combining the vertical amplitude obtained by numerical simulation with the change in the vorticity magnitude and direction. The results showed that the flow velocity around the lower surface of the airflow was reduced, resulting in significantly lower vorticity at the exact position of the lower chord deflector plate and web deflector plate section compared to the original section. The web deflector plate and the lower chord deflector plate broke the vortex shedding mode in the wake flow region, and the vortex shedding frequency was far away from the self-oscillation frequency of the double-deck truss girder, thus suppressing the VIV.  
vortex-induced vibration; double-deck truss girder; deflector plate; wind tunnel test; CFD simulations +
Footnote †: journal: Applied Sciences