esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
## 3 Materials and Experimental Setup
### Evaluation Criteria  
To assess the accuracy of the hyperspectral image classification, three evaluation metrics are commonly used: (1) overall accuracy (OA), (2) F1-score, and (3) average F1-score (AF) [57]. OA is related to the ratio of correctly classified samples to the total samples, which provides an intuitive interpretation of the accuracy (Equation (5)). In contrast, to evaluate the per-class accuracy, the F1-score of each class was used in our study,which takes into account both recall and precision (Equation (4)) [57]. The F1-score helps to evaluate the accuracy of the majority and minority classes. Additionally, AF, a supplementary accuracy evaluation index, is also listed in our results.  
\[\text{F1}=\frac{2\times\text{P}\times\text{R}}{\text{P}+\text{R}},\;\text{P}= \frac{\text{TP}}{\text{TP}+\text{FP}},\;\text{R}=\frac{\text{TP}}{\text{TP }+\text{FN}} \tag{4}\]  
\[\text{OA}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+\text{ FN}} \tag{5}\]  
where FN is the false negative, FP is the false positve, TN is the true negative, and TP is the true positive.
## 3 Pressure Sensor Concept and Design
### Hydrostatic Sensor (HS)  
The first configuration consists in inserting a cylindrical aluminum plate into the cap cavity free space, as presented in Figure 3a. The plate dimensions (28 mm in diameter and 10 mm in thickness) are smaller than the one of the cap cavity (35 mm in diameter and 14 mm in height). The plate is made of aluminum because of its intermediate compressibility and its chemical resistance to the DOZ. During a hydrostatic test, the plate is surrounded in all directions by the fluid pressure to be measured. In this technique, it is supposed that the sensor is not subjected to any mechanical loading  
Figure 2: Water collecting cap concept: (**a**) initial configuration; (**b**) modified configuration with drainage system, shortened specimen and protection membranes; and (**c**) drainage cap design with micro-holes, plug and sealing joint.  
since its dimensions are smaller than the cap cavity dimensions. Furthermore, a deformable gauge is mounted on one of the plate surfaces serving to measure the deformation on the sensor during each test. Figure 3b shows the water collecting cap, the hydrostatic sensor, the plug and the O-ring sealing joint. The aluminum plate has the following elastic characteristics: Young's modulus \(E\) equal to 80 GPa and Poisson ratio \(\
u\) equal to 0.35. Theoretically, the plate bulk modulus \(K\) is equal to 90 GPa (referring to Equation (1)). Since this configuration generates a hydrostatic homogeneous stress around the sensor plate, the relation between the applied pressure \(p\) and the resultant volumetric strain \(\epsilon_{v}\) is given by Equation (2), where \(\epsilon_{v}\) is related directly to the gauge deformation \(\epsilon_{x}\) by means of Equation (3). If we consider that this sensor is subjected to a confinement pressure equal to 500 MPa, the plate will deform homogeneously and in every direction with a magnitude \(\epsilon_{x}\) equal to 0.184%, which corresponds to a volumetric strain \(\epsilon_{v}\) equal to 0.552% (positive in compression).  
The relation between the Young's modulus \(E\), the Bulk modulus K, the confining pressure \(p>0\) and the plate deformations \(\epsilon_{v}<0\) are given below:  
\[K=\frac{E}{3(1-2\
u)} \tag{1}\]  
\[p=K\epsilon_{v} \tag{2}\]  
\[\epsilon_{v}=3\epsilon_{x} \tag{3}\]
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Aloha-Based Protocols
#### 5.1.4 Enhanced Aloha (E-Aloha)  
When sensor nodes of a telemetry application transmit data readings, they are usually configured to send packets in a periodical way, with a fixed time interval between messages. In this scenario, the E-Aloha protocol has been proposed as a simplified version of Aloha. At the time of sending, nodes simply initiate transmission, with no additional control to avoid collisions; a confirmation of reception is also not considered in the protocol. For some applications, nodes may end up with the same time interval between packets, in which case the nodes will collide permanently, with no effective communication. To avoid this situation, E-Aloha defines a time window--considerably longer than the packet transmission time--located around the fixed sending time. Within this window, each node selects randomly a new sending time, thus reducing the chances of permanent collisions among nodes using the same time interval.  
Figure 6 illustrates the behavior of the protocol for nodes originally colliding over the same time interval (see Figure 6a), and the corrections made through the use of a time window around the fixed sending times (see Figure 6b). E-Aloha was introduced in [43] for use in satellite systems devoted to telemetry such as Argos.  
The performance evaluation of this protocol considered the periodic traffic characteristic of typical telemetry systems [43]. The reported results indicate that for a \(PLR=10^{-1}\), the protocol achieves a normalized throughput of \(S=0.091\), considering a channel load \(C=0.101\). The performance is, then, very similar to the one reported for S-Aloha (\(C=0.1\) for a \(PLR=10^{-1}\)), with the advantage that E-Aloha does not require time synchronization. Furthermore, there is less complexity in implementing E-Aloha than for Aloha, in particular since E-Aloha does not require a specific logic implemented at the receiver. By not requiring ACK, this protocol is energy efficient when the load on the channel is very low (\(C\sim 0.1\)). For higher loads, the performance of the protocol drastically decreases due to the high number of collisions.  
The simplicity of this protocol is desirable for the implementation on the IoT terminal node. It actually behaves well for the reported channel loads found in the systems where the protocol has been implemented. However, E-Aloha may lack the necessary scalability to support the future IoT networks.  
Figure 6: Example of E-Aloha operation where three users send packets at a rate of \(\frac{1}{T}\) packets/s. In (**a**), the users wait a fixed time (\(T\)), in consequence, there is a permanent collision between the users 1 and 2; whereas in (**b**) there is a time window (\(L\)) for selecting a new random sending time in order to avoid permanent collisions. Figure adapted from [43].
# A Systematic Literature Review of Reliable Provisioning for Virtual Network Function Chaining
## 4 Results of the Systematic Literature Review
### Different Strategies Used for Reliable Service Function  
One of the key issues in the deployment of Service Function Chains (SFCs) is reliability, especially as VNFs are more prone to software failure and connectivity errors [18; 25; 34]. Moreover, telecom network services are required to have even higher availability in the Service Level Agreements (SLA) compared to previous IT applications that demand 99% and 99.9% [35]. SLA violations cost providers penalties and decrease the quality of the  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Consideration** & **Criteria for Inclusion** & **Eligibility** \\ \hline Publication year & No restrictions. Results from 2014 to 2023 & The final range after screening is from 2015 to 2021 \\ \hline Relationship with the subject & Papers related to NFV, VNF, and SFC. The paper discusses provisioning & Included when the main contribution is related to reliable provisioning \\ \hline Type of document & The research is published in IEEE, ACM, SCO-PUS, Web of Science, or Google Scholar & The research outcome is published in proceedings and journals \\ \hline \hline \end{tabular}
\end{table}
Table 1: Screening of papers.  
Figure 3: overview of the search process.  
service a customer experiences. For example, IT downtime and data recovery cost IT businesses in North America USD 26.5 billion in revenue each year [36]. Therefore, ensuring the reliability of the deployment service function chain and increasing its availability is of high importance.
## 3 Results
### Groundwater Level and the Rising Sea Level  
GWT and mean sea level both increased over time, especially from 2015 onwards (Figure 5a), with a weak statistical relationship (\(R^{2}\) = 0.11; Figure 5b). It is worth noting that the flux tower is approximately 20 km away from the coastline, which explains the weak relationship between GWT and mean sea level. We also observed a positive correlation between seawater level during tidal cycles and GWT (figure not shown). The relationship between GWT and sea level during low tide was higher (\(R^{2}\) = 0.32) than during high tide (\(R^{2}\) = 0.19).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Site** & **Dependent Variable** & **Best Model Predictors** & **Multiple \(R^{2}\)** & **Inter-Cept** & **Coefficient** & **F Value** & \(p\)**Value** \\ \hline \multirow{8}{*}{US-NC4} & GPP & & 0.66 & 6.23 & & & \\  & & Tair & & & 2.35 & 119.29 & \(<\)0.0001 \\  & & GWT & & & \(-\)1.04 & 47.93 & \(<\)0.0001 \\  & & Rn & & & 7.43 & 24.09 & \(<\)0.0001 \\  & & P & & & \(-\)3.68 & 6.60 & \(<\)0.0001 \\ \cline{2-8}  & RE & & 0.71 & 6.39 & & & \\  & & GWT & & & & 2.36 & 255.13 & \(<\)0.0001 \\  & & Rn & & & 3.21 & 2.85 & \(<\)0.0001 \\  & & P & & & \(-\)1.07 & 2.44 & \(<\)0.05 \\ \cline{2-8}  & NEE & & 0.29 & 0.16 & & & \\  & & Tair & & & 0.02 & 42.31 & \(<\)0.0001 \\  & & Rn & & & 0.67 & 30.84 & \(<\)0.0001 \\  & & GWT & & & \(-\)1.03 & 12.74 & \(<\)0.001 \\  & & P & & & 0.16 & 12.70 & \(<\)0.001 \\ \hline \end{tabular}
\end{table}
Table 2: Results of generalized additive modeling (GAM) analysis to detect the best models and rank predictor variables e.g., air temperature (Tair), net radiation (Rn), groundwater table depth (GWT), and Precipitation (\(p\)) according to their ability to explain variation in gross primary productivity (GPP), ecosystem respiration (RE), and net ecosystem exchange (NEE). Daily data from 2009–2019 were used in the analysis. In order to avoid model over-parameterization, the second-order Akaike information criterion was used to determine the best smoothing dimension with an upper limit equal to 3. We report the coefficient of the fitted model with values for each spline term in turn.  
Figure 4: Linear relationships between groundwater table depth (GWT) and gross primary productivity (GPP), ecosystem respiration (RE), and net ecosystem exchange (NEE) during the growing (green circles and equations) and non-growing seasons (orange circles and equations) from 2009–2019. Daily data was used in the analysis.  
Figure 5: Panel (**a**) is the time series of groundwater table depth (GWT) and mean sea level using monthly data from 2009–2019, while panel (**b**) is the linear relationship between GWT and mean sea level.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 2 Materials and Methods
### Parallel Model Training and HPC Integration
#### 2.4.2 MPI for Parallel Algorithm Execution  
While Spark handled data distribution and coarse-grained parallel tasks, MPI was employed for fine-grained parallel algorithms, especially related to deep learning models. One critical use of MPI in our pipeline was to parallelize hyperparameter optimization (HPO) for the bi-LSTM and f-LSTM models. We used Optuna for HPO, and by launching multiple Optuna trials across MPI ranks, we could explore the hyperparameter space (e.g., LSTM layer sizes, learning rates, dropout rates) much faster than serial search. Each MPI rank would run an independent training trial of the f-LSTM on a subset of data or with a unique hyperparameter set, and the results were gathered to decide the next set of trials. This MPI-based scatter-gather approach yielded near-linear speedups in the HPO phase, as each rank utilized both its CPU and (if available) GPU resources to train a model variant concurrently. MPI's dynamic task allocation ensured that if one rank finished a trial early, it could request a new trial, thereby balancing the load and avoiding idle processors [10; 11]. In practice, hardware memory limitations meant we ran a smaller number of parallel trials (e.g., five parallel trials at a time) to avoid GPU memory saturation, but the parallel approach still yielded a several-fold speedup in the hyperparameter search phase. Beyond HPO, MPI was also used to parallelize the training of multiple reservoir-specific models in experiments (for instance, training an LSTM for each reservoir simultaneously). In those cases, each rank was assigned a subset of reservoirs to train on, and the master process synchronized results, such as training time and error metrics.
# Experimental Investigation into Permeable Asphalt Pavement Based on Small-Scale Accelerated Testing
## 2 Materials and Methods  
### Model Mobile Load Simulator MMLS3  
The experimental investigation employed a one-third scaled accelerated loading apparatus, designated MMLS3, to evaluate model permeable pavement performance as illustrated in Figure 1. The MMLS3 system incorporates four reciprocating wheels generating 2.7 kN axle loads with a 0.7 MPa tire contact pressure. The technical specifications of the MMLS3 configuration are detailed in Table 1. Scaled and prototype systems demonstrate analogous mechanistic responses when maintaining dimensional similitude. For equivalent pavement materials with proportional layer thicknesses and linear elastic material behavior, prototype-to-model correlations require the adherence to scaling criteria: length, 1:\(n\); load, 1:\(n^{2}\); time, 1:\(n\); materials properties, 1:1; pressure on the surface, 1:1 (where \(n\) is scale factor, and \(n=3\) in this study). The scaled surface and base layer thicknesses were maintained at one-third of the prototype dimensions. To obtain full-scale displacements, the load duration in the model pavement should be equal to that of the prototype multiplied by the same scale factor used for the geometric dimensions. In other words, if the full scale and small scale use the same speed, the displacement of the small scale should multiply the scale factor, which will be equal to the displacement of the full scale [11]. In fact, the higher speed can result in the lower critical strain response in a full-scale APT [30;31]. The small-scale MMLS3 can produce the opposite results [28]. However, speed was outside the scope of this study. The maximum speed of MMLS3 is 9 km/h. The higher speed can reduce the duration, and 7.5 km/h was chosen for safety in this study. In addition, due to the small size of the scaled specimens, the rigid constraints of the mold walls could restrict the lateral deformation of the material, leading to stress concentration at the edges, which could lead to an overestimation of the rutting resistance or fatigue life. At the same time, the rigid boundaries could also suppress interlayer slippage and lateral plastic flow, causing the rutting deformation pattern to deviate from that of the actual pavement. In this study, to avoid the significant influence of the boundaries on strain and deformation, the strain and deformation under the wheel track were mainly investigated.
## 3 Results and Discussion
### Characterization of the Zeolite Samples
#### 3.1.3 FT-IR Analysis Results  
The results of FT-IR spectroscopy of fresh iron-Z-A, phosphate adsorbed iron-Z-A, and phosphate desorbed iron-Z-A can be seen in Figure 3. In the 4000-400 cm\({}^{-1}\) region, the IR spectra of the zeolite frameworks can be seen. The IR vibration in the zeolite spectra of fresh Z-A backbone consists of -OH stretching and bending vibration at 3467.78 and 1653.85, respectively. These are the bands which are formed due to the OH groups. These OH groups are as; (i) OH groups which balance the negative charge by attaching to Fe\({}^{3+}\), (ii) OH groups taking place at defects, and (iii) Si-O-H groups at lattice ends [47] Si-O(Si) and Si-O(Al) bridge bond's asymmetric stretching vibration can be seen at 993.27 due to. Si-O-Si and Si-O-Al bridge bond's symmetric bridge vibrations at 734.83 and 668.29 respectively, bending vibrations of O-Si-O bond in antiphase at 441.67, bending vibration of O-Si-O and Si-O-Si  
Figure 2: EPR spectra of (**a**) pristine Iron-Z-A, (**b**) P-adsorbed Iron-Z-A, and (**c**) P-desorbed Iron-Z-A.  
bridge bond's symmetric stretching vibrations (complex band) at 569.92 can be observed. The internal vibration at 993 cm\({}^{-1}\) indicates O-T-O asymmetrical stretching modes. The shifts of these bands IR spectra in the backbone of Iron-Z-A, phosphate adsorbed iron-Z-A, and phosphate desorbed iron-Z-A are shown in Table 1. According to [PERSON] et al. (2016) [48], the peak at 480-560 cm\({}^{-1}\) is a characteristic of iron-nano zeolite [48]. The change in intensity of -OH bonds of P-adsorbed Iron-Z-A and P desorbed Iron-Z-A from fresh Iron-Z-A shows the participation of the hydroxyl group in phosphate adsorption. The revival in -OH bond intensity of P desorbed Iron-Z-A affirms its participation in the desorption of phosphate. Hence, the involvement of -OH bond in both adsorption and desorption of phosphate hints the existence of the ligand exchange mechanism. Further fall in intensity after adsorption of phosphate and revival of peak intensity after desorption of phosphate also suggests adsorption and desorption of phosphate by Al.
## 2 Materials and Methods
### Methods
#### 2.3.3 LSTM Model Construction  
A long short-term memory neural network (LSTM) can learn feature representation directly from the original input sequence through end-to-end learning, avoiding the complexity of manual feature extraction using traditional methods. This feature makes LSTM perform well in time series data processing. The LSTM model structure in the present study includes a sequence input layer, an LSTM layer, a ReLU activation layer, a dropout layer, and a full-connection layer. The sequence input layer processes data for seven features. The LSTM layer comprises a total of 256 units, which only output the final state of the sequence. The ReLU activation layer and two fully connected layers (128 and 64 neurons, respectively) further process the data, adding nonlinearity. The output layer consists of a single neuron and is suitable for regression tasks.
## 5 Discussion  
### Technical Analysis of the GIBD of BWSCs  
Gas injection and brine discharge is a critical step in converting brine salt caverns into gas storage facilities. Unlike traditional single-well salt cavities, the GIBD method for butted wells in old salt caverns is closely linked to the utilization of residue. According to the analysis in Section 3.1, without considering the utilization of the void space in the residue, the TITD method is similar to the GIBD of single-well salt cavities and represents the most mature technology. However, this method is obviously difficult to adapt to the current situation of high residue content in old domestic BWSCs. Given the background of in-depth research on the gas storage utilization of residue void space and the demand for increased storage capacity, the other two GIBD methods are more suitable for the present reality. The OIOD and TIDD methods have advantages in residue space utilization. Depending on the residue content and the shape of the salt cavern, these two methods are the key technical means for the integrated and comprehensive utilization of the brine cavity and residue space of butted wells [44]. Although there is still an insufficient understanding of the gas storage efficiency in residue voids at present, the technical feasibility of these two GIBD methods has been preliminarily verified in the field. In China, ongoing pilot tests for the reconstruction and utilization of BWSCs and residue include the Zhangxing gas storage and Yunying compressed-air energy storage projects. For the Zhangxing gas storage project, the reconstruction work of old BWSCs has been carried out. The first-phase gas injection operation was completed using the OIOD method, with a gas injection volume of approximately 70 million cubic meters. This verifies the feasibility of reconstructing old BWSCs into gas storage facilities and the fluidity of brine in the connecting channels and residues. The Yunying compressed-air energy storage facility is also being built by reconstructing a pair of old BWSCs. The GIBD operation is being carried out using the TIDD method, and the first-phase project has been put into operation, which verifies the dual feasibility of the TIDD method for old BWSCs and the utilization of residue space [45]. The large-height-difference BWSC studied in this paper has significant structural differences compared with the conventional equal-height BWSC, but it still meets the requirements for the above two GIBD methods. The pilot project tests provide a practical basis for the gas injection and brine discharge methods for the old large-height-difference butted-well salt caverns in this study.
## 1 Introduction  
Breast cancer surgery has undergone profound changes in the last few decades [1, 2]. Breast conservative surgery (quadrantectomy, BCS) and the adoption of new techniques for \"conservative\" mastectomy (skin-sparing and ripple-sparing) are clear examples of oncological surgery aimed at improving the patient's aesthetics and quality of life.  
The most important studies that showed a substantial equivalence between BCS plus adjuvant radiotherapy of the entire breast and mastectomy were conducted some decades ago [3; 4]. Their conclusions were crucial in changing the approach to local breast cancer treatment. Nevertheless, the scenario today has substantially changed. Nowadays, in this context, the preservation of the mammary organ by BCS has become the treatment of choice, thanks to the excellent results and optimal tolerability achieved [5; 6]. However, various population-based studies have shown that mastectomy is still considered a reliable treatment option chosen by some breast cancer patients in daily clinical practice. The breast cancer survival rates are now higher. This is probably due to several factors, such as the smaller tumor dimensions, fewer patients with positive lymph nodes at diagnosis, as well as the more effective therapies available, including neoadjuvant therapies based on specific neoplastic characteristics [7; 8].  
In recent years, several retrospective analyses of large contemporary datasets have shown the superiority of treatment with BCS over treatment with mastectomy in the early stages of breast cancer. BCS was found to provide better outcomes than mastectomy for eligible patients with stage I breast cancer, in terms of specific survival and overall survival, and thus should be offered as a preferred treatment option [9; 10; 11]. Moreover, other studies focused on young women [12; 13] and on triple-negative breast cancer [14; 15] have shown similar results.  
In some studies, locoregional recurrence percentages have been found to be higher after BCS than after mastectomy [16]. This has been further confirmed in triple-negative breast cancer and positive receptor 2 of the human epidermal growth factor (HER2) [15], even though [PERSON] et al. reported contradictory results [14]. Unfortunately, the locoregional recurrence percentages were rarely provided in the cited retrospective analyses, as the data were not always reliable. Therefore, the presumed superiority of mastectomy over BCS in reducing locoregional recurrence has not been clearly shown. Moreover, the subgroup of patients that could benefit from BCS remains unclear.  
In this paper, we analyzed the disease-free survival of the patients treated in our institute, differentiating between patients who received breast conservative therapy and patients who received mastectomy. We studied evidence gathered for different subgroups of patients treated at our institute in the last 20 years.