esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Compressor Re-Design  
Since the vehicle displacement and power (998 CC) are assigned, the natural choice is to use (and possibly slightly reconfigure) the compressor currently installed, i.e., the Garrett GT12 model, whose operational map is shown in Figure 1. In view of the modifications implemented on the compressor geometry and briefly described below, it is useful to summarize here the conceptual design procedure [5; 6; 7; 8; 9]:  
1. The nameplate volumetric flow rate \(\hat{Q}_{nom}=\hat{m}/\rho_{nom}\) is calculated at the rotor intake, at standard \(p_{0}\) and \(T_{0}\) conditions (298 K, 1 bar);
2. The design compression ratio \(\beta\) is imposed, and the polytropic specific work is calculated by: \[W=c_{p,av}\ T_{0}\ \left(\ \beta^{\frac{k-1}{k\psi_{p}}}-\ 1\right)\] (1)
3. Using the design peripheral speed of the current industrial version of the machine, \(U_{des}\), the required head coefficient is obtained: \[\psi_{2}=\frac{W_{EUL}}{\eta_{m}U_{des}^{2}}\] (2)
4. If the obtained \(\psi_{2}\) value is acceptable (i.e., falls within the range of values historically adopted in similar machines with technically satisfactory results), only one stage will be sufficient. This is the case for the present situation.  
The next step is to perform a preliminary design of the stage, i.e., to define all of its dimensions and shape ratios, choosing the materials and proceeding to a final design [10; 11; 12]. In our case, it was decided to modify the compressor design adopting a tandem configuration (Figure 2) consisting of an axial inducer followed by a radial exducer, maintaining the same inlet and outlet section diameters as in the Garrett GT12. The details of the necessary modifications and the DOE-CFD-aided configuration optimization are reported in detail in [13], and only the final results will be presented here. The modified compressor has a higher compression ratio than the original unit with an improved surge margin and a lower sensitivity to choking: its characteristic is shown in Figure 3, together with the corresponding engine operating points. The results of the preliminary design of the compressor are displayed in Table 1.  
Figure 1: Garrett GT12 Compressor operative map and operating points; on x-axis the corrected mass flow, on y-axis the pressure ratio.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**ICE rpm** & _in_ [kg/s] & **Compressor rpm** & \(\mathbf{\beta_{c}}\) & \(\mathbf{T_{in}}\) [K] & \(\mathbf{T_{out}}\) [K] & \(\mathbf{P}\) [W] & \(\mathbf{\eta}\) \\ \hline
2000 & 0.021 & 145,000 & 1.42 & 298.4 & 344 & 920.71 & 0.68 \\
3500 & 0.0408 & 180,000 & 1.64 & 297.5 & 354 & 2474.39 & 0.8 \\
5500 & 0.0619 & 210,000 & 1.78 & 297.5 & 366.8 & 4444.96 & 0.82 \\ \hline \end{tabular}
\end{table}
Table 1: Tandem version of the GT12 compressor operating points (new design).  
Figure 2: CAD (Computer Aided Design) rendering of the new tandem compressor.
## 5 Experiments
### Ablation Study  
We conducted five ablation experiments by sequentially removing the _Role_, _Task_, _Terminology_, _Rules_, and _Examples_ layers. These experiments aimed to validate that the shearer maintenance entity recognition method, guided by multi-level collaborative prompts, improves model accuracy and mitigates output instability.  
To evaluate the impact of the five sub-prompts on model performance, we conducted ablation studies. In these, each sub-prompt was removed individually and the model's performance was compared. The results of the ablation experiments are shown in Figure 6. As shown in Figure 6, (i) when the _Role_ sub-prompt is removed, the F1 score for entity extraction decreases by approximately 13% compared to our proposed method. This is because assigning a specific role to the model enables it to better understand the context of the problem and user expectations, leading to more accurate and consistent responses. The _Role_ sub-prompt helps the model focus on domain-specific information and knowledge, enabling it to provide more specialized answers. Removing the _Role_ sub-prompt introduces noise, resulting in a decline in the F1 score for entity extraction. (ii) When the _Task_ sub-prompt is removed, the F1 score decreases by approximately 16%. This is because the Task sub-prompt helps the model clearly understand the task objectives and expected outcomes, thereby reducing misunderstandings and errors. (iii) When the _Terminology_ sub-prompt is removed, the precision and F1 score for entity extraction decrease by 18.44% and 10.04%, respectively. This is because the _Terminology_ sub-prompt provides the model with relevant definitions of terms in the shearer maintenance domain. After removing the _Terminology_ sub-prompt, the model lacks domain-specific knowledge and constraints, resulting in a significant decline in precision. (iv) When the _Rules_ sub-prompt is removed, the F1 score for entity extraction decreases by approximately 9.0%. This is because the _Rules_ sub-prompt emphasizes output consistency and ensures data completeness, enabling the model to focus on specific requirements. (v) When the _Examples_ sub-prompt is removed, the F1 score for entity extraction decreases by 12%. This is because _Examples_ serve as part of the training data, helping the model better learn the characteristics and patterns of the specific task. By providing clear examples, the model can more accurately understand the task details and boundary conditions, reducing errors when handling complex or ambiguous tasks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **P** & **R** & **F1** \\ \hline BiLSTM-CRF & 82.97\% & 81.85\% & 82.41\% \\ BERT-BiLSTM-CRF & 86.75\% & 85.40\% & 86.07\% \\ MCP & 87.45\% & 87.08\% & 87.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of entity recognition experiment.  
The experimental results above indicate that the five designed sub-prompts all enhance model performance to varying degrees, demonstrating the effectiveness and necessity of each sub-prompt.
## 6 Conclusions  
This paper proposes a new signal perception architecture for SOP positioning system and completed the implementation. By separating the signal perception function, it reduces hardware and energy resources waste caused by multiple devices continuous working in traditional method. The core is an CNN-based SOP classification model and signal time-frequency joint representation. We use CWT to complete the signals time-frequency joint representation, and designs a CNN-based model for feature extraction and classification to time-frequency images. This paper introduces the NL-PL joint training method, which can suppress overfitting to noise data effectively. Compared with the previous work with SURF + K-means + SVM method, the recognition rate is higher (more than 97%), and solved the problem of recognize mixed signals in same time-frequency image. We also build a prototype system through USRP and LabVIEW, and verified the perception ability of 2.4 GHz ISM signals (WIFI, Bluetooth, ZigBee) in the underground parking lot. The experiment result proved the effectiveness of the design. The perception architecture proposed in this paper can be extended to other opportunistic signal and realize almost all frequency domain and all kinds SOP perception. The efficient realization of the SOP perception function can promote the further integration and upgrade of the SOP positioning system.  
In response to the problems exposed in research and experiments, the subsequent research directions are as follows:  
1. Introduce noise suppression methods to solve the perception failure when the target signal power is at the same level of noise, and improve the sensitivity of perception;
2. Select USRP equipment with better performance to realize wider bandwidth SOP perception;
3. Combine the perception unit proposed with SOP positioning system to carry out positioning experiments.  
Conceptualization, Z.D. and H.Q.; methodology, Z.D. and H.Q.; software, H.Q.; validation, H.Q., Y.L., and E.H.; formal analysis, Z.D.; investigation, E.H.; resources, E.H.; data curation, Y.L.; writing--original draft preparation, H.Q.; writing--review and editing, H.Q.; visualization, H.Q.; supervision, Z.D.; project administration, Z.D. and H.Q.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
Figure 34: Relationships between the reduced power consumption, percentage of energy efficiency improvement, and the number of USRP switched to standby mode.
## 3 Experiments and Results
### Band Extraction and Selection Methods  
In order to further illustrate the importance of selecting the most meaningful bands, the accuracy when the PCA, VGBS and the SSBS approaches are employed is reported in Table 2. PCA reduces the data into the most important wavelengths that account for the largest variance in the dataset. Using eigenvalue decomposition of the covariance matrix of the HS data cube, the image that corresponds to the first, second, and third largest eigenvalue are selected as the first three principal components. Only these components are selected for further processing, because they contain the largest variance of information.  
The VGBS algorithm was evaluated for different numbers of bands (5, 10, 15), but only the results obtained with 10 bands have been reported, because they were found to be the most competitive. When 10 bands are selected, the mean error for estimating the 2D displacement is 0.42 (\(\pm\)0.05) mm when SURF is used as a feature detector algorithm, while an error of 0.27 (\(\pm\)0.06) mm is found when DELF is employed instead. When 5 and 15 bands are selected, the error increases by 3% and 1%,  
Figure 6: (a) RGB (Red Green Blue) color image obtained from the hyperspectral radiance image of subject no. 1. (b) a clipped version of image (a) with the clip level taken from the brightest region.  
respectively, when SURF is applied, and an error growth of 3% is found with DELF. The plots provided in Figure A1 in the Appendix A show the detailed results obtained with the VGBS algorithm.  
For the SSBS algorithm, the output number of the selected bands is determined by the input HS image itself [31] and selected bands cannot be ranked. Due to the similarity in the nature of the input images, the number of selected bands using the SSBS method is equal to 16, 15 and 14 for five, six and four subjects, respectively.  
As an example, Figure 7 illustrates the selected bands for subject no. 8 when the SSBS algorithm is used. Figure 8a,b show the matching results when PCA is used as a band extraction algorithm. Alternatively, Figure 8c,d portrays the results when the band selection algorithms are used. The matched features, which are detected using DELF, are depicted in red, and those detected by SURF are shown in green. After performing the geometric verification with RANSAC to ensure sufficient inliers, the region found after the breathing of the subject is drawn in red, as in Figure 8a. As visualized in Figure 8a-d, a higher amount of inliers is found when DELF is employed for both band extraction and selection.  
Figure 7: Ten selected bands are illustrated for subject no. 8. The bands are selected by using the Saliency-Band-Based Selection (SSBS) algorithm [31] adapted to our dataset.
## 3 Results and Discussion
### Average Delay Comparison
In contrast, GPSR and GPSR-DEM show a proportional increase in delay with higher traffic generation, likely due to congestion and routing inefficiencies under higher network load. QOS-AODV demonstrates an inverse relationship with traffic generation, showing slightly improved delay performance as traffic increases, although it still lags behind TOM profiles. The performance of TOM in minimizing delay aligns with findings in the recent literature on low-latency routing protocols in VCNs. Studies like those by [PERSON]. [29] demonstrate that optimizing for delay in highly dynamic environments can significantly improve real-time data transmission, especially in scenarios requiring rapid response times, such as vehicular safety applications. The low delay observed in TOM profiles makes it comparable to other optimized protocols like QoS-Aware Multipath Routing (QAMR) mentioned by [PERSON]. [30], which also focuses on reducing latency in the worst-case scenario up to 95%. DEM's moderate performance in delay is a result of its balancing approach, which, while improving over the baseline protocols, cannot achieve the same low delays as TOM due to its multi-parameter optimization strategy.
# Priority-Based Content Delivery in the Internet of Vehicles through Named Data Networking +
## 4 The Priority-Based Forwarding Framework  
In this section, we present our proposed NDN forwarding framework, called pNDN, conceived to meet the priority requirements of IoV traffic.  
As graphically sketched in Figure 2, our solution is intended to be deployed in a \"clean-slate\" manner, i.e., directly on top of the access technology (802.11 OCB, 3G/LTE). It leverages the legacy NDN supporting tables and consists of the following novel features:* an advanced naming scheme that tags NDN packets with a name prefix identifying the application latency requirements and the consumer preferences;
* a freshness attribute in the Interest that captures the consumer expectation for the data retrieval;
* a smart strategy layer that decides where and when NDN packets should be transmitted, forwarded and (if necessary) retransmitted. To this aim, it encompasses the following modules: (i) the face selection, which decides which network face(s) is (are) forwarding an Interest; (ii) a freshness-based forwarding protocol, which accounts for the freshness specified by the consumer to decide if the Data can be served by the content store; (iii) a timing algorithm, implemented only on top of the 802.11 OCB face, which ensures the efficient and prioritized multi-hop delivery of Interest and Data packets in the V2V/V2R domain; and (iv) an Interest retransmission scheme, which allows a consumer to retransmit Interests whenever the Data packets are not received within a predefined time interval.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 3 Problem Formulation and System Model
### Problem Formulation
\[D_{t}=x_{1}d_{t-1,a_{1}}+x_{2}d_{t-1,a_{2}}+\cdots+x_{n}d_{t-1,a_{n}}. \tag{6}\]  
The variables \(x_{1},x_{2},\ldots,x_{n}\) are natural numbers such that:  
\[ \begin{split}& x_{1}+x_{2}+\cdots+x_{n}=1,\\ & x_{1}x_{2}\ldots x_{n}=0.\end{split}  \tag{7}\]  
The optimal TRxP selection strategy is given by the following optimization equation. \[\arg\min_{a\in A}D_{t}\] subject to \[(1)P_{a_{1}}\geq P_{t_{1}},\] \[(2)P_{a_{2}}\geq P_{t_{2}},\] \[\vdots \tag{8}\] \[(3)P_{a_{n}}\geq P_{t_{n}},\] \[(4)R_{a}\geq P_{a},\] \[(5)P(W\geq t)\leq\delta.\]  
The parameters in the constraints are defined as follows. Constraints (1)-(3) are related to the power strength received from the available TrXPs. \(P_{a_{i}}\) is the power received from TRxP \(a_{i}\), which should be at least equal to the threshold value \(P_{t}\). This constraint requires that users receive an acceptable power level from the TRxP to consider it for selection and later perform RA through it. In constraint (4), users draw a random number, \(R_{a}\), that should be greater than the system parameter, \(P_{a}\), broadcast by the TRxP, enforcing a prohibition on excessively massive connection scenarios for accepted RA requests. Before considering the current TRxP for selection, users must first make sure they pass this requirement. Constraint (5) describes the delay-budget of the user. Weight (delay) estimation is another task in solving the equation given in (8). An exact analytical expression representing the weights first depends on the RACH model, and second, considers strong assumptions from queuing theory. In this study, however, we employ learning techniques that enable the proposed method to predict (estimate) the weights, which also can be advantageous in that they do not assume a predefined RACH model. A deep RL formulation for the delay is provided in the next section.
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 7 The RINA Domain
### QoS Support Evaluation

Table 4: Synthetic application traffic flow characteristics and QoS class assignment. Figure 8: QoS Cubes defined in the SPN (**left**) and MPN (**right**) DIFs for full RINA scenario. QoS differentiation based on delay. Indeed, outcomes perceived by most urgent synthetic traffic flows (HD Video Call, Online Gaming and VoIP) become quite constant, which does not happen for the least urgent ones (File Sharing and Interactive traffic flows). As for the experienced packet losses, full RINA allows for an effective differentiation, reaching a maximum value of 1.42% for Interactive traffic. Packet losses are significantly lower, around 0.00064%, for file-sharing traffic, with the same Urgency but a higher Cherish level. In contrast, configuring the TCP/IP-like case neither succeeds in providing QoS differentiation in terms of end-to-end latency nor in terms of packet losses. In fact, although the applications may be able to request a given QoS differentiation at the edge of the network (i.e., SPN), it cannot be guaranteed at the core (i.e., MPN). The final result is that all types of applications experience similar loss and latency levels under every offered load scenario.
## 4 Results
### HIL Testing: Validation of Gravity Compensation Model  
Real-time experiments have been conducted to validate the dynamic model obtained form the characterization of the Rigid-Body-Tree shown in Figure 2(a).  
The test aims to verify the consistency between the torque estimated by the SEA sensors (obtained by evaluating the deflection of the elastic element) and the torque calculated using the dynamic model of the exoskeleton obtained with the methodology presented in Figure 4. To evaluate the performance of the exoskeleton, authors decided to explore a wide portion of the workspace by moving the robot in transparency mode and recording the executed movement (learn and replay mode). This recorded movement is then executed by the exoskeleton without the presence of a user, in position control (so that the SEA sensors detect only the gravity torque and not the interaction torque with the user). From the collected data of the experiment, it is possible to perform a comparison between the measurement of the gravity torque obtained from the SEA sensors and the one estimated using the MATLAB Gravity Torque Toolbox (based on the characterized dynamic model).  
In Figure 2(b) are shown the Gravity Torques computed from the dynamic model and recorded by SEAs for the spherical joint complex (J3, J4, J5). The computed RMSEsand absolute errors, between the SEA measure and the gravity compensation model are reported in Table 1.
## 3 The Experimental Results  
Experiments were carried out in this part to verify the efficacy of the suggested approach. First, the relevant characteristics and the sources of the CT sections used in this work are presented. Next, the EDSR-Tiny network's training set is described, consisting of low-resolution CT slices and high-resolution CT slices with artifacts eliminated. The training outcomes of EDSR-Tiny are then displayed. By comparing the PSNR and SNR of CT sections before and after reconstruction, the method's viability is finally confirmed.
## 4 Evaluation
### Concurrency
#### 4.1.3 Configurations  
The following pipeline configurations are being evaluated:  
* _REIP Hybrid_: image writer blocks are executed in the same task as the corresponding object and motion detection blocks. This is our primary configuration as depicted on Figure 4a.
* _REIP Multiprocessing_: each block is executed in an independent task (a full multiprocessing configuration).
* _REIP Threading_: all blocks are being executed in the same (main) process but, by design, in their own independent threads.  
We further customise the configurations by using different serialisation strategies (Pickle/Pyarrow/Plasma) for inter-process block connections. The system is also intentionally throttled by enabling only four CPU cores to measure smaller variations in performance overhead under these different configurations. For the most fair comparison with other representative frameworks (Ray and Waggle), we implement a thin wrapper emulating the REIP API for each framework (more details in Section 4.1.5). We use the same wrapper for the REIP SDK too (REIP Backend in Table 2), which does not include different connection strategies or other advanced features of the REIP SDK (hence a slightly better performance compared to the full REIP SDK).
## 2 Materials and Methods  
### Test Site  
The test site is located at the construction site of a deep foundation pit in a sewage treatment plant in Lanzhou, with a total planned land area of 108,211.0 m\({}^{2}\). The construction scale of the project in the near term, by 2025, is a processing capacity of 300,000 m\({}^{3}\).d\({}^{-1}\), and in the long term, by 2035, a processing capacity of 400,000 m\({}^{3}\).d\({}^{-1}\). The intended project involves an integral buried steel-concrete box structure with a burial depth of approximately 18 to 20 m. The base gradually rises from south to north, with the site's leveled elevation ranging from 1526.50 m to 1529.00 m. The project's importance level is classified as Level I. The site is situated in the high floodplain zone on the left bank of the Yellow River, with a relatively shallow groundwater depth, which affects the project to a certain extent. The surrounding area is characterized by the presence of municipal roads, municipal pipelines, and buildings, contributing to moderately complex environmental conditions. Frequent human engineering activities have led to general environmental degradation, resulting in a Level II complexity rating for the site. The geological composition of the site includes various types of rock and soil, along with relatively thick layers of artificial fill that are unevenly distributed. This non-uniform distribution has led to significant variations in the properties of slope materials. As a result, the complexity level of the foundation is categorized as Level II. The soil layers at the site, from top to bottom, consist of miscellaneous fill, loess-like silt, cobbles, heavily weathered mudstone, and moderately weathered mudstone, as depicted in Figure 1. The stable groundwater depth at the site ranges from approximately 4.8 to 8.3 m, with corresponding elevations between 1519.27 and 1521.24 m. The elevation of the bottom plate of the underground structure ranges from 1507.70 to 1512.30 m, positioned within the influence range of the groundwater up to a certain height. Consequently, anti-floating anchors need to be employed to mitigate buoyancy effects.  
The test anchor is positioned within the medium-weathered mudstone layer, specifically at the base of the buried concrete box. The medium-weathered mudstone is characterized by a muddy composition with a thick laminated structure. It exhibits relative integrity but is prone to fracturing and softening, especially when exposed to water. The physical and mechanical parameters of the medium-weathered mudstone are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Gravity** **(kN\(\cdot\)m\({}^{-3}\))** & **The Angle of Internal** **Friction (\({}^{\circ}\))** & **Force of Cohesion** **(kPa)** & **Elastic Modulus** **(GPa)** & **Poisson Ratio** **Strength (MPa)** \\ \hline
21.3 & 34.7 & 80 & 1.7 & 0.18 & 0.88 \\ \hline \end{tabular}
\end{table}
Table 1: Physical mechanical parameters of rock.