esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Preoperative Assessment and Inclusion/Exclusion Criteria  
Patients affected by non-metastatic muscle-invasive or high-risk non-muscle-invasive bladder cancer and fit for orthotopic neobladder, considering age, life expectancy, comorbidities, and patient preferences, were considered suitable for the procedure. Exclusion criteria were locally advanced cancer, hydronephrosis, renal or liver function impairment, and inflammatory bowel disease. All patients had preoperatively assessed stage N0M0 BC and underwent a standard routine preoperative work-up, consisting of chest-abdominal CT scan, blood exams, and cardiological assessment. All cases were previously discussed by a multidisciplinary team which included a urologist, oncologist, and radiotherapist. Written informed consent was obtained from all patients involved in the study. All data were prospectively entered into an institutional database. Particularly, overall operative time, bladder reconfiguration time, and hospitalization were analyzed. All procedure-related complications were recorded and classified according to the Clavien-Dindo score. Neobladder reconstructions were performed by the same surgical team to avoid bias related to differences in surgical skill or learning curve.
## 2 Related Work  
In the literature, the workflow allocation (WFA) problem has been proven NP-hard [17]. Users may not be able to get the full benefits of the system because of excessive communication overhead, under-utilization of cloud resources, or improper workflow task allocation. WFA in the Cloud faces many challenges due to the precedence constraints, heterogeneity and dynamism in workflow tasks, optimized mapping decisions, and QoS constraints.  
Therefore, researchers in the domain developed many models for the problem at hand, considering one or more objective parameters in cloud computing. The workflow models can be either static (i.e., decisions made prior to allocation) or dynamic (decisions made at run time).  
Static WFA models [9; 18] assume the structure of the workflow, the estimated communication and execution time, VM configuration, etc., as fixed and known. Therefore, these models outperform other dynamic models [20; 21; 22; 23; 24]. On the other hand, the dynamic allocation models [21] allocate the cloud resources on run time in a dynamic manner. Dynamic allocation models are able to handle uncertainties but may compromise in order to achieve global optimization. For workflow applications in the cloud environment, massive intensive data processing is faced. Therefore, static workflow allocation models are used to optimize the QoS parameters and can be further categorized into a single workflow and multiple workflow allocation models.
# Fault Diagnosis of Induction Motor Using Convolutional Neural Network
## 3 Proposed Fault Diagnosis Method for Induction Motors  
### CNN-Based Fault Diagnosis System  
The proposed fault diagnosis system consists of a vibration sensor for measuring the vibration signals generated by an induction motor, as shown in Figure 3; data collection part for digitizing and storing the vibration signals obtained from the vibration sensor; and a CNN-based fault classification unit that classifies the types of faults generated in the induction motor. The CNN model receives vibration data obtained from the induction motor and classifies them into three states, namely, normal, rotor fault, and bearing fault. In addition, a GUI was developed using LabVIEW to enable users to diagnose faults of induction motors efficiently and intuitively.
## 4 RL-Based Decision Making Solution
### RL-Based Solution
#### 4.2.4 Reward Function  
The reward in the proposed RL-based solution is defined in Equation (9), and it is designed to find the best video streaming path according to the four QoS metrics: link available bandwidth, delay, jitter and loss rate. The intention is to find a route with minimum delay, loss, and jitter while prioritising links with large bandwidth to enhance the QoE of the video streaming service. It is noteworthy that different influence factors (IFs) can impact the satisfaction degree of video streaming services. This research concentrates on the network-related IFs, which refer to the QoS factors because it has been demonstrated that these metrics significantly influence the QoE [40].  
\[R=w_{1}*\frac{1}{B_{l}}+w_{2}*D_{l}+w_{3}*L_{l}+w_{4}*J_{l} \tag{9}\]It is worth noting that these metrics have different units (e.g., the available bandwidth in bit per second and delay in millisecond), which have an impact on the learning efficiency thus, each metric value is normalised to united ranges and scales [41]. The values \(w_{1},w_{2},w_{3}\), and \(w_{4}\in[0,1]\) are tuning weights assigned to a determined metric during the reward computation, where \(w_{1}+w_{2}+w_{3}+w_{4}=1\). Note that the tuning weights of QoS factors are specified in the agreement with the quality standard bounds and their relative importance degree, which are provided in [34] in this fashion, \(L_{l}\) 58.9%, \(J_{l}\) 15.1%, \(D_{l}\) 14.9% and \(B_{l}\) 11.1%.
# Optimized Architecture for Efficient OFDMA Network Design
## 3 Analytical Model for OFDMA Networks
### Scheduling Policies  
The scheduling algorithm is responsible for allocating radio resources in each frame to the active users. In wireless networks, this allocation can take into account the quality of the radio link. Initially, three conventional policies are considered to distribute all available resources (i.e., all slots in each frame) among the active stations:  
* Resource fairness: Allocates slots equally among active users, regardless of their radio conditions.
* Throughput fairness: Ensures that all active users achieve the same instantaneous throughput.
* Opportunistic scheduling: Allocates all resources to the active users with the best channel conditions (i.e., the highest MCS).  
The fourth type of scheduling is the throttling regime, which is proposed to model a performance metric in the [29] called Maximum Sustained Traffic Rate (_MSTR_). This metric specifies an upper limit on the maximum throughput that can be offered to a user.  
Using the analytical model, the performance parameters associated with the scheduling algorithms, both for conventional policies and the throttling regime, will be determined.
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 4 Results
### Field Results from VEN\(\mu\)S and Sentinel-2  
Table 1 summarizes the statistics of terminations detected using the VEN\(\mu\)S and Sentinel-2 time-series data. Both datasets produced reliable estimations of termination dates with a small bias (<2 days) and a very high R\({}^{2}\) (>0.98). The mean uncertainties are within 4 days for VEN\(\mu\)S and 7 days for Sentinel-2. The VEN\(\mu\)S time-series captured all harvest operations for both years. There are about 7% of terminations missed from Sentinel-2. One out of 29 terminations (3.4%) from VEN\(\mu\)S and three out of 29 terminations (10.3%) from Sentinel-2 were falsely detected. Results show that VEN\(\mu\)S outperformed Sentinel-2 in all metrics due to higher temporal resolution.
## 2 Materials and Methods  
### Extraction of Olive Oil Bodies  
Olive paste (OP) from Leccino cultivar drupes, provided by the National Research Council--Institute for Agricultural and Forest Systems in the Mediterranean (CNR-ISAFoM) based in Perugia (Italy), was used to extract the OOBC (Olive Oil Bodies Cream) containing OBs. The OP was delivered frozen and stored at \(-20\)\({}^{\circ}\)C before the experiments. The reagents used in the extraction process were: Sucrose BioUltra > 99.5%, Sodium Chloride BioUltra > 99.5%, citric acid monohydrate 99.5% and sodium hydroxide 98%. All the reagents were purchased from Merck. First, we applied an extraction protocol with some modifications which was borrowed from the patent WO2007115899 A1 [24] filed for oleaginous seeds using a sucrose/sodium chloride aqueous buffer. The patented protocol consists of four main steps. First, we performed wet grinding of the raw material by using a solution of water and sucrose at 0.6 M (grinding medium, GM, obtained by Mixer type XB98, Ceado, Venezia, Italy). The following three steps were carried out using three different floating buffers, which were aqueous solutions of sucrose and sodium chloride. Specifically, these floating buffers were FB\({}_{1}\) (0.2 M sucrose, 1 M NaCl), FB\({}_{2}\) (0.1 M sucrose, 1 M NaCl) and FB\({}_{3}\) (0.2 M NaCl) [24]. Each suspension was mixed for 2 min to obtain a homogeneous suspension (by Professional Thermal mixer HotmixPRO Combi Cold, Combi, Italy), and then centrifuged at \(3226\times g\) for 40 min [24]. After each centrifugation, the cream (solid layer) was separated and resuspended in the next FB. Subsequently, in the last centrifugation step, the OOBC were extracted. Based on the patent held by [PERSON] et al. [24], a new protocol was developed for the OP, optimising the composition of the grinding and buffer solutions and the operative steps to maximise the extraction yield. We called this first new protocol \"Method 1\". Taking into account the results of Method 1, we defined a second novel extraction protocol, so-called \"Method 2\". The protocol extractions were repeated three times. The yield was evaluated as the ratio between the total weight of the extracted product and the initial weight of the olive paste:  
\[\text{Yield, wt.}\%=\text{W}_{\text{f(product)}}/\text{W}_{\text{i(raw material)}} \tag{1}\]
## 1 Introduction
### 1.1 Maturity Classification  
Different sensors have been employed for nondestructive maturity classification of fruits, including electronic noses [8], radio frequencies [9], and spectral systems [10; 11; 12; 13; 14]. Since most robotic systems incorporate a vision system on the end effector (eye-in-handconfiguration) for fruit detection and localization [4; 15; 16], using vision for maturity classification as well is preferable. Furthermore, machine vision enables to predict both internal and external quality attributes [10].  
Color is often used as a maturity indicator for several crops [3; 5; 17]. It has been used, for instance, in classifying maturity of fruits such as strawberry [18; 19], apples [20], tomatoes [21; 22; 23], dates [24], plums [25], mango [26], and pineapple [27]. Many of these fruits mature in a uniform manner, allowing to assess the maturity from a single viewpoint. Sweet peppers, on the other hand, do not mature uniformly [7; 13; 28]; some parts of the pepper can be fully colored red or yellow, while other parts are still green (see examples in Figure 1). These heterogeneous ripening patterns hinder the possibility to determine maturity accurately from a single viewpoint since the information from this single side-view might be misleading. Although a single bottom-view can be used to differentiate between mature and immature peppers [29; 30], a viewpoint directly below the pepper (facing upwards) is most likely not applicable for robotic harvesting because this viewpoint is often occluded by leaves [31]. Furthermore, it is not efficient since it requires the robot to move the camera from the bottom of the pepper all the way up to the peduncle to harvest the pepper when it is mature.  
Adding viewpoints for maturity classification of sweet peppers has proven to yield improved performance [31]. Taking additional viewpoints, however, takes time, leading to increased cycle times. To make a robotic harvester economically feasible, it is important to limit the cycle time [32]. Therefore, an additional viewpoint should only be acquired when deemed profitable. Consequently, it is important to dynamically select the viewpoints that are expected to reveal the most information about the pepper's maturity [31]. [PERSON] et al. [31], however, did not deal with the dynamic viewpoint selection.
## 6 SAR for Antennas on Human Body
Test phones, wearables, and wireless connectivity are required for electromagnetic field security. Several factors can affect the SAR. Some of them are the distance from the \"phantom,\" the antenna volume, and tissues. The size of the phantom, its separation distance from the antenna, and the types of tissues it represents all influence SAR values. This impacts SAR evaluation and EMF equipment security. Regulating these features creates safe, compliant equipment. Table 9 provides safety- and performance-focused wearable antenna designs with their size, operation frequency, and SAR for 1 g and 10 g tissue models. The rectangular patch antenna with an inset feed line of 51 \(\times\) 45 mm\({}^{2}\) operating at 2.45 GHz has SAR values of 0.54 W/kg 1 gm and 0.486 W/kg 10 gm, whereas the T-shaped 70 \(\times\) 70 mm \begin{table}
\begin{tabular}{c c c c} \hline
**SAR (W/kg)** & **2.45 GHz** & **5.8 GHz** & **SAR (W/kg)** \\ \hline
1 g & 17.5 & 9.3 & 1 g \\ \hline
10 g & 8.99 & 4.08 & 10 g \\ \hline \end{tabular}
\end{table} 
Table 8: SAR value summary for 0.5 watts of input power. antenna has values of 0.0323 W/kg (1 gm) and 0.0153. The reverse U&T slot patch antenna with dimensions of 58 \(\times\) 63 mm\({}^{2}\) has low SAR values of 0.012 W/kg 1 gm and 0.23 W/kg 10 gm. The 100 \(\times\) 100 mm\({}^{2}\) circular patch and 60 \(\times\) 60 mm\({}^{2}\) folded ring-shaped antennas provide safe SAR levels for 2.45 GHz and 5.8 GHz dual-frequency applications. The 60 \(\times\) 50 mm\({}^{2}\) ground coplanar waveguide antenna has a 0.25/0.7 W/kg SAR at 7-28 GHz. SAR-compliant wearables provide compact, efficient, and safe healthcare and IoT solutions with wireless connectivity.
## 3 Results and Discussion
### Removal Efficiencies of Solids and COD
#### 3.3.3 Nitrogen and Phosphorus
Figure 8 shows the average concentrations and removal rates of total phosphorus in the bioreactor for each tested HRT. In a completely mixed bioreactor, a significant removal rate of total phosphorus is theoretically not supposed to be registered as this nutrient is submitted to the conversion between the liquid phase to the solid phase and _vice versa_, and from inorganic species to organic species and vice versa; however, the total phosphorus remains globally constant [34]. In this context, the removal rates registered in this work may be explained by the retention of biomass inside the bioreactor due to the low mixing rate. The retention of solids occurs not only as biomass attached to the physical parts of the bioreactor (biofilms) but also in the upper part of the liquid (floating material and scums). The presence of solids in the floating material and scums turned the collection of homogeneous samples in the effluent a \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Assay** & **Sampling Point** & \begin{tabular}{c} **TKN** \\ **(mg N.L\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **N-NH\({}_{4}\)\({}^{+}\)** \\ **(mg N.L\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **o-N** \\ **(mg N.L\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **N-NH\({}_{4}\)\({}^{+}\)** \\ **(\% TKN)** \\ \end{tabular} &
\begin{tabular}{c} **o-N** \\ **(\% TKN)** \\ \end{tabular} \\ \hline HRT4 & Influent & 2844 & 1969 & 875 & 69 & 31 \\ HRT4 & Effluent & 2592 & 2406 & 186 & 93 & 7 \\ HRT5 & Influent & 3017 & 1663 & 1354 & 55 & 45 \\ HRT5 & Effluent & 2249 & 2129 & 120 & 95 & 5 \\ HRT6 & Influent & 3134 & 1662 & 1472 & 53 & 47 \\ HRT6 & Effluent & 2337 & 2159 & 177 & 92 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Concentrations of TKN, N-NH\({}_{4}\)\({}^{+}\), o-N, and relative proportions of N-NH\({}_{4}\)\({}^{+}\), o-N in the influent and effluent of the DF bioreactor for all experimental assays. big challenge. Additionally, they worked as another compartment of solid material for the retention of phosphorus inside the bioreactor. No values were found in the literature on total phosphorus with which the data obtained in the present work could be compared.
# Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times
## 3 BBR Operation Principles
### BBRv3  
BBRv3 was introduced in July 2023 [16] and it officially obsoleted both previous versions, although it is still not available in any Linux kernel by default. The third version aims to reduce queuing delay and act more fairly towards loss-based CCs. The major updates of BBRv3 include fixing bugs that hindered bandwidth convergence in the presence of shallow and deep buffers and tuning the performance parameters involving gains and loss threshold.  
The first major bug observed in BBRv2 interrupted bandwidth probing shortly after a loss or ECN signal was detected and the inflight_HI parameter was set. This issue stemmed from a circular dependency between the maximum bandwidth and maximum inflight data. Consequently, BBRv2 struggled to achieve fair bandwidth share when competing with loss-based CCs. It also hindered achieving high bandwidth utilization after congestion events. BBRv3 addresses this issue through persistent bandwidth probing, thereby continuing until either the loss rate or ECN mark rate exceeds predefined tolerance thresholds of 1%.  
Figure 3: BBRv2 state machine of PROBE BW phase and transition conditions.  
The second bug was observed when the buffer size exceeded 1.5*BDP, and there were no loss or congestion signals. BBRv2 encountered difficulty in achieving equitable bandwidth allocation due to a fixed cvnd_gain, which hindered slower flows from increasing their sending rate. BBRv3 addressed these issues by modifying the \"gains\" during the PROBE BW phase. During the _UP_ phase, the cvnd_gain was raised from 2.0 to 2.25, thus enabling more effective adjustments in sending rates. Secondly, the pacing_gain was adjusted from 0.75 to 0.9 during the _DOWN_ phase. This adjustment aids slower flows in consistently utilizing adequate bandwidth, thus improving convergence to an equitable share more swiftly.  
BBRv3 has tuned several configuration parameters. During the STARTUP phase, the cvnd_gain was lowered from 2.89 to 2.0. This parameter significantly influences the inflight data limit during this phase. This adjustment has been motivated by [21]. Similarly, the pacing_gain was reduced to 2.77 based on the analytical reasoning presented in [22]. Upon exiting the STARTUP phase, inflight_HI is determined by selecting the maximum value between the estimated BDP and the highest number of packets successfully transmitted during the last RTT. Moreover, the transition condition has been altered, and the threshold for consecutive packet losses has been adjusted from eight to six. These modifications have led to a reduction in the experienced drop ratio and queuing delays.
## 5 Conclusions  
This work focused on the nonlinear free vibration and thermal buckling temperature of functionally graded fiber-reinforced composite laminated (FG-FRCL) beams for three different types of boundary conditions, i.e., simply supports, clamped-clamped and clamped-free supports. The nonlinear partial differential equations associated with the problem were derived based on the Euler-Bernoulli beam theory and von [PERSON] geometrical nonlinearity. Such nonlinear equations were reduced to a single equation by neglecting the axial inertia. The Galerkin method was adopted to discretize the governing nonlinear partial differential equation in the form of nonlinear ordinary differential equation, which was solved analytically using a semi-inverse method to study the nonlinear free vibration time response of FG-FRCL beams. A parametric investigation focused on the effect of power-index, different lay-ups, uniform temperature rise and slenderness ratio on the nonlinear natural frequency and phase trajectory of the selected structural components. A further systematic investigation investigated the sensitivity of the thermal buckling response to different power-indexes and lay-ups. Based on such numerical investigations, the following final remarks can be summarized:  
1. FG-FRCL beams showed the highest nonlinear natural frequency response, followed by FGM beams and composite laminated beams;
2. The nonlinear fundamental frequency increased for an increased power index, whose effect became more pronounced for lower slenderness ratios;
3. The fundamental frequency decreased for an increased temperature, especially for higher slenderness ratios;
4. Based on a parametric evaluation of the response for different reinforcement lay-ups, FG-FRCL beams with [0/0/0] lay-ups showed the highest nonlinear natural frequency and thermal buckling temperature, followed by [0/90/0], [90/0/90] and [90/90/90] lay-ups, in sequence;
5. An increased power index provided an increased critical buckling temperature of the system, whereas the nonlinear oscillation velocity of FG-FRCL beams assumed the highest value followed by FGM beams and composite laminated beams, which, in turn, featured the lowest oscillation velocity;
6. An increased power index expanded outward the phase trajectory and yielded an increased oscillation velocity. At the same time, for an increased temperature, the phase trajectory shrank inward and the oscillation velocity decreased, while the system maintained its own stability.  
Conceptualization, M.A., H.H., F.T. and R.D.; Methodology, F.T. and R.D.; Validation, H.H., F.T. and R.D.; Formal analysis, M.A., F.T. and R.D.; Investigation, M.A., H.H. and R.D.; Resources, F.T.; Writing--original draft, M.A. and H.H.; Writing--review & editing, F.T. and R.D.; Supervision, F.T. and R.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 1 Introduction
However, the Kalman filter is extremely dependent on forecasting the next time based on the information of the last sequence point and the current position information. Neural networks and deep learning have strong nonlinear mapping, self-learning, and adaptive abilities. The disadvantage is that they are very sensitive to the initial network's weight, and there is a local minimization problem. When the initial network weight is not uniform, the training results may be different, so this method is applicable to most trajectory prediction scenarios. Since there is no uniform standard for the structure of a neural network, the appropriate network structure should be selected according to the specific situation in practical application [12]. We proposed to use a sun vector and a one-dimensional convolutional neural network combined with a long short-term memory network (CNN-LSTM) hybrid neural network model as a method for predicting the trajectory of the pitch axis of the space turntable. First, according to the sun position calculation model, we calculated the ideal solar position value from the sun vector data and then used the ideal solar position value of the pitch axis, pitch axis motor speed, current, and position error signal data as the input data of the model. We then selected a specific step of the time sliding window and predicted the position of the pitch axis at the next time. It was built on a model of CNN-LSTM, the prediction sequence was set with an adaptive Adam optimizer, and the simulating telemetry data of a two-dimensional turntable was used for training. We used RMSE and MSE as performance evaluation indicators.