esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Holistic Trash Collection System Architecture
### System Components
#### 4.1.3 Sensors at Central Garbage Collection Facility  
Sensors will exclusively be installed within those trash bins located at the central garbage collection center, serving the sole purpose of gauging garbage levels. These sensors will leverage IoT technology to promptly transmit data to the cloud. They are expected to exhibit a prolonged operational lifespan, resilience to environmental factors, and minimal maintenance requisites. The sensors will employ either infrared or ultrasonic technologies for ascertaining the fill levels of the bins. Infrared sensors, in particular, harness infrared light--electromagnetic radiation with a wavelength exceeding that of visible light--to detect and quantify the bin's fill level. These sensors function by emitting infrared light into the trash bin and subsequently measuring the intensity of reflected light. As the trash accumulates, the reflected light diminishes, facilitating the sensor's determination of the fill level. Ultrasonic sensors, on the other hand, use high-frequency sound waves to detect and measure the fill level of the trash bin. These sensors work by emitting high-frequency sound waves into the trash bin and measuring the time it takes for the sound waves to bounce back. As the trash level rises, the time it takes for the sound waves to bounce back increases, allowing the sensor to determine the fill level.  
These sensors will transmit observed data to the cloud-based analytics system to predict bin fill levels. Upon detecting that a container is either full or in need of attention, the sensors will promptly alert the garbage collection staff in real time, ensuring efficient communication without unnecessary repetition. Both infrared and ultrasonic sensors are widely employed in applications necessitating non-contact detection and measurement of physical attributes, including fill-level assessment in trash bins, distance measurement, and object detection, among others. The selection of sensor technology is contingent upon multiple factors, including cost considerations, performance prerequisites, and prevailing environmental conditions. Consequently, we have the flexibility to employ both sensor types in our application based on the specific requirements and conditions of the implementation.
## 3 The Proposed Delay-Doppler Block Division Multiplexing
### Signal Generation
The discrete-time transmitted DDBDM samples modulated by the inverse fast Fourier transform (IFFT) are expressed as follows:  
\[s_{l}[m]=\frac{1}{\sqrt{M}}\sum_{k=0}^{M-1}\sqrt{P_{k}}\mathbf{X}_{\text{tf}}[ k,l]e^{j2\pi knm/M} \tag{12}\]  
where \(m=0,\ldots,M-1\), \(l=0,1,\ldots,N-1\). Subsequently, \(s_{l}[m]\) is converted into a discrete-time sample vector \(\mathbf{s}[q]\) by parallel-to-serial (P/S) conversion:  
\[s[q]=\text{vec}(s_{l}[m]) \tag{13}\]  
where \(q=0,1,\ldots,MN-1\), \(\text{vec}(\cdot)\) denotes the vectorization operator. (7) Add CP and convert to analog signal. To combat the effects of multipath on the transmitted symbols, the last \(L_{\text{CP}}\) samples of \(s[q]\) are typically added to the beginning of the frame as a cyclic prefix (CP):  
\[\mathbf{s}_{\text{CP}}=[s[MN-L_{\text{CP}}],\ldots,s[MN-1],s[0],\ldots,s[MN-1 ]]^{T} \tag{14}\]  
The duration of the CP is greater than the maximum delay spread of the multipath channel. Finally, the discrete-time transmitted samples are converted into a continuous-time baseband signal \(s(t)\) through a digital-to-analog converter (DAC):  
\[s(t)=\sum_{q=0}^{MN+L_{\text{CP}}-1}\mathbf{s}_{\text{CP}}[q]\cdot\Pi\bigg{(} \frac{t}{T_{s}}-q\bigg{)} \tag{15}\]  
where \(T_{s}\) is the sampling interval. Steps (6) and (7) are essentially regarded as the Heisenberg transform. Similarly to the power spectral density (PSD) expression for OFDM [44; 45], the PSD of the DDBDM signal can be written as follows:  
\[G(f)=\frac{T}{M}\sum_{k=0}^{M-1}\left|\frac{\sqrt{P_{k}}\sin[\pi(f-f_{k})T]}{\pi( f-f_{k})T}\right|^{2} \tag{16}\]  
It is composed of communication subcarriers and navigation subcarriers, each allocated a different power.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 2 Materials and Methods
### Methodology and Implementation
#### 2.2.5 Seasonality Parameters  
We also used the TIMESAT software to extract seasonal parameters such as the start of season (SOS), peak of season (POS), and end of season (EOS). This was achieved by employing the method based on seasonal amplitude as described by [53]. Accordingly, SOS and EOS were determined when the fitted curve reached a specific fraction of the difference between the base level and the POS. Following the PPI calibration by [39], we applied thresholds of 0.4 for NDVI and 0.25 for PPI to determine SOS and thresholds of 0.5 for NDVI and 0.15 for PPI to determine EOS.
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 5 Evaluation
### Sensor Node Current Consumption  
#### 5.1.1 Advertisement-Based Approach  
By using the values shown in Table 1 and Equations (1)-(4), we calculate the average current consumption of the sensor node in the advertisement-based approach. Figure 9 illustrates the obtained  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Packet Type** & **Packet Size (Bytes)** & **Payload Size (Bytes)** \\ \hline Data channel PDU (minimum size) & 10 & 0 \\ Data channel PDU (maximum size) & 37 & 20 \\ Advertising channel PDU & 47 & 31 \\ (non-connectable type, maximum size) & 23 & – \\ Advertising channel PDU (connectable type) & 23 & – \\ \hline \hline \end{tabular}
\end{table}
Table 3: Packet and payload sizes considered in the evaluation. For a maximum sized data channel PDU, the maximum payload size is 20 bytes of attribute notification. The maximum payload allowed in the non-connectable advertisement type (used in the advertisement-based approach) is 31 bytes.  
results, as a function of _advInterval_, covering the whole range of values for this BLE parameter. We plot the results for \(N=1\), and for \(N=3\).  
As expected, the average current consumption decreases with _advInterval_, since the sleep intervals have a greater duration for a greater _advInterval_, while the duration of the advertising event remains constant. For the same reason, while for low _advInterval_ values the difference between \(N=3\) and \(N=1\) is significant, such difference tends to decrease as _advInterval_ increases.  
The average current consumption falls below 26 \(\mu\)A (i.e., the lifetime achievable with a 230 mAh coin cell battery is greater than 1 year, see Section 5.2) for an _advInterval_ equal to or greater than 3.06 s and 1.52 s, for \(N=3\) and \(N=1\), respectively. The value of \(N\) controls a trade-off between sensor node average current consumption and reliability. While using the three advertising channels (\(N=3\)) increases current consumption, it may be relevant to face interference or multipath issues, which may occur in urban environments.  
Finally, note that sensor node current consumption is independent of the channel BER, as the sensor node will transmit the advertisements in any case (regardless of whether they are correctly received by the mobile entity or not).
# Biomechanical Optimization of Lumbar Fusion Cages with a Porous Design: A Finite Element Analysis
## 3 Results
### Cage Stress  
The maximum von Mises stress within the cages was quantified across six motion modes as well as displacement of L3-L4 segment simulation, as visualized in Figure 6a and details in Table S3. The classic fusion cage (C-Cage) exhibited the highest stress levels across all loading conditions, with values ranging from 18.14 MPa in A-Compression to a peak of 63.04 MPa in flexion, indicative of the substantial mechanical demand placed on its structure. In contrast, the personalized P-Cage demonstrated a moderate reduction in stress, with values decreasing to 13.08 MPa in A-Compression and 52.9 MPa in flexion, suggesting that personalization alone offers some biomechanical advantage. Further reductions were observed with the porous designs, where the G-Cage recorded stresses of 11.43 MPa in A-Compression and 49.09 MPa in flexion, and the V-Cage achieved the lowest values, at 10.58 MPa and 45.41 MPa, respectively. Compared to the C-Cage, the V-Cage reduced maximum stress by 41.7% in A-Compression and 28.0% in flexion, underscoring its superior load-bearing capacity.  
Stress distribution contour maps (Figure 6b) revealed that the C-Cage experienced pronounced stress concentrations, particularly at the cage-endplate interface, which could predispose it to fatigue failure. Conversely, the V-Cage displayed a more uniform stress distribution across its surface, attributable to its Voronoi-inspired porous architecture, which enhances load transfer efficiency and mitigates stress peaks. The G-Cage, while also porous, exhibited intermediate stress levels and distribution patterns, suggesting that the Gyroid structure, though effective, is less optimal than the Voronoi design in minimizing stress concentrations.
## 4 Routing Protocol
### Data Transmission  
The data transmission phase is almost the same as the SPIN protocol. Data is sent to the requesting node immediately after request is received by the source node. If the requesting nodes are intermediate nodes instead of the sink node then the negotiation phase is repeated. Therefore, the intermediate sensor nodes broadcast ADV for the data with the modified hop distance value. The sending nodes correct the hop distance field with its own hop distance value and add that in packet format of the ADV message. The process continues until the data reaches the sink node.
# An Updated Review of Smac Mimetics, LCL161, Birinapant, and GDC-0152 in Cancer Treatment
## 7 Anti-Cancer Molecular Mechanisms of Birinapant (TL32711)  
Birinapant is a second-generation bivalent Smac mimetic, first synthesized in 2014 [76; 77]. Birinapant has better tolerability for treating solid tumors compared with the first-generation bivalent Smac mimetic, and it preferentially targets cLAP1, relative to cLAP2 and XIAP [76]. The anti-cancer potency of birinapant has been investigated in acute myeloid leukemia, melanoma, colorectal cancer, ovarian cancer, breast cancer, head and neck squamous cell carcinoma, hepatocellular carcinoma, glioblastoma, and breast cancer [23; 77; 78; 79; 80; 81; 82; 83; 84]. At the molecular level, birinapant binds the BIR domains of IAPs and promotes the degradation of TNF receptor-associated factor (TRAF)-bound IAPs, resulting in the induction of caspase-8-dependent apoptosis (Figure 3) [85]. In addition, birinapant degrades IAPs, activates the non-canonical NF-\(\kappa\)B signaling pathway once the IAPs are degraded, and stabilizes mitogen-activated protein kinase 14 (MAP3K14) (NF-\(\kappa\)B-inducing kinase), resulting in the activation of the non-canonical NF-\(\kappa\)B signaling pathway [84; 86]. [PERSON] et al. demonstrated that apoptosis repressor with caspase recruitment domain, an apoptosis suppressor, decreases the anti-cancer effect of birinapant by inhibiting the cIPA1/MAP3K14 signaling pathway in acute myeloid leukemia [87]. Birinapant induces cell cycle G2/M arrest in head and neck squamous cell carcinoma; however, the underlying molecular mechanism of action remains unclear (Figure 3) [79; 88]. In acute lymphoblastic leukemia, birinapant induces RIP-1-dependent necroptosis (Figure 3) [83]. When birinapant is combined with emricasan, a caspase-8 inhibitor, necroptosis is induced in acute myeloid leukemia [89]. [PERSON] et al. demonstrated that birinapant-induced TNF activates CD8\({}^{+}\) T cells and natural killer cells, resulting in cancer cell death (Figure 3) [90]. Targeting programmed death-ligand 1 (PD-1) upregulates TNF production; therefore, the combination of birinapant and PD-1 blockade increases TNF production and promotes the anti-cancer potency of immune therapy [90].
# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 1 Introduction
### Machine Learning Classifiers  
ExtraTreesClassifier [7] is an ensemble learning method that combines multiple randomised decision trees. It improves classification performance by randomising both samples and features. Default parameters include n_estimators = 100, criterion = 'gini', and max_depth = None. Its robustness to overfitting and effectiveness in high-dimensional spaces make it a reliable choice for complex datasets.  
LogisticRegression [7] is a linear model optimised for binary classification problems. It uses a logistic function to estimate probabilities and is characterised by parameters such as penalty = '12', C = 1.0, and solver = 'lbfgs'. This model is easy to interpret and performs well with linearly separable data.  
RandomForest [7] is another ensemble classifier that constructs multiple decision trees and aggregates their outputs to enhance prediction accuracy. Default settings include n_estimators = 100 and criterion = 'gini'. The model is highly effective in reducing overfitting and can handle both classification and regression tasks.  
GaussianNB [7] is a Naive Bayes classifier adapted for continuous data by assuming Gaussian distributions for features. This simple yet efficient model requires minimal computational resources and performs well on smaller datasets or when independence assumptions are valid.  
AdaBoost [7] is a boosting algorithm that combines weak classifiers iteratively, weighting misclassified samples to form a strong classifier. Parameters include n_estimators = 50 and learning_rate = 1.0. Its adaptability and ability to reduce bias and variance are key advantages.  
DecisionTree [7] splits data hierarchically based on feature thresholds, creating interpretable and easily visualised models. Default parameters are criterion='gini', splitter = 'best', and max_depth=None. It handles both categorical and numerical data with minimal preprocessing.  
Figure 1: Schematic of Medical Interview System for Diagnosis—A mobile app has been developed, which implements a type–speech-to-text system and includes learned models that recognise individual conditions based on the interview. Where necessary, the system validates the results by querying and providing a list of available ailments. It also filters further queries based on the information received from the patient.  
GradientBoosting [7] is a sequential boosting algorithm that minimizes a loss function to improve prediction accuracy. Key parameters include loss = 'deviance', learning_rate = 0.1, and n_estimators = 100. Its ability to handle moderate-sized datasets effectively is a notable advantage.  
The classifiers were implemented using Python's scikit-learn library [7], with hyperparameters initially set to default values. Fine-tuning was performed to optimise performance metrics such as accuracy, precision, recall, and F1-score. The models were systematically evaluated to identify the most suitable classifier for deployment, ensuring robust and efficient performance adapted to the requirements of the study.
# DAB Signal Preprocessing for Passive Coherent Location
## 2 Materials and Methods
### DAB Signal Preprocessing
#### 2.4.3 Duplication of MSC Symbols in Place of Null, PR, FIC Symbols  
After finding the start of the frame (\(n_{0}\)) as described previously, the signal samples carrying the null, PR, and FIC symbols from both the reference and the surveillance channel are replaced by the copies of the selected MSC symbols from the previous frame  
\[\begin{array}{c}x(n)x(n-N_{\rm s}),\\ x_{\rm s}(n)x_{\rm s}(n-N_{\rm s}),\\ n=n_{0}-N_{\rm margin}-N_{\rm null},\ldots,n_{0}+4\times N_{\rm symb}\end{array} \tag{7}\]  
where the time shift \(N_{\rm s}\) results from the index \(N_{\rm C0}\) (\(5\leq N_{\rm C0}\leq 72\)) of the first MSC symbol that was copied from the previous frame into the position of the Null symbol  
\[N_{\rm s}=(77-N_{\rm C0})\times N_{\rm symb}. \tag{8}\]  
The frame structure after this modification is shown in Figure 9.  
The side effect of partial signal duplication (7) is the introduction of additional side peaks in the autocorrelation plot of the signal (with adjacent pairs of peaks resulting from the cyclic prefix insertion [17]), as depicted in Figure 10 for \(N_{\rm C0}=69\). However, the position of these peaks is strictly determined (8) and thus can be modified by selecting the copied symbols (\(N_{\rm C0}\)).
# A State-of-the-Art Review on the Study of the Diffusion Mechanism of Fissure Grouting
## 5 Current Status of Experimental Research on Grouting Modeling of Fissured Rock Bodies
### Future Directions for Research Development  
Experimental research on grouting models in fissured rock masses is the basis for rock grouting engineering design and practical operation plans. There are various experimental methods, including studying the properties of fissures and grout, the effects of grouting parameters on grouting efficiency, and the deformation and stress characteristics of rock masses after grouting. This can help to better understand the effectiveness of rock grouting and develop more optimized grouting plans. In the future, research in this field should focus on developing more efficient and accurate experimental methods, developing advanced grouting technologies, establishing more comprehensive and accurate rock models, and improving the control and monitoring of grouting circuits to achieve more comprehensive results. We recommend conducting a more in-depth and detailed analysis of the mechanisms and treatment methods for poorly grouted rock masses, the distribution patterns of fissures, and the exploration of optimization methods for grouting parameters and grouting quality detection technology.  
Figure 8: Schematic diagram of the test system [83].  
1. For experimental research on fissured rock masses, exploring the spatiotemporal evolution of the rheological properties of grout and porosity at any testing point and time (in space and time) can help to better reveal the micro-mechanisms of the evolution of grout diffusion pressure. However, current research on high-polymer grouting experiments mostly focuses on the diffusion of fissures and small pores, while there is relatively little research on large porous gravel media. The influence of water flow on high polymer diffusion is mostly ignored. The experimental research mainly focuses on the relationship between grouting parameters, the form of diffusion, and the radius of the diffusion solidification body without considering the spatiotemporal evolution of the medium's dynamic response during grout diffusion.
2. There are still many factors that affect the diffusion of cement slurry or high polymers in fissured media, and relatively few factors have been examined in actual experiments. Therefore, more factors need to be considered, such as introducing smarter devices, developing new experimental systems, and examining the influence of different water flow rates on grout diffusion simultaneously. The influence of high-polymer foaming agent dosage, preheating temperature, gel time, and other factors on diffusion also needs to be examined.
3. There are many materials available for grouting technology currently, and in the future, new grouting materials and composite materials can be developed specifically for fissured rock mass grouting. These new materials can be used for the development of experimental systems to further improve grouting efficiency.
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 3 Voucher-Based Addressing
### Interface Enforcement Modes  
Each interface employing VBA must always have the option to set a single local Interface Enforcement Mode (IEM) which determines its handling of NDP messages and VBA. IEMs are a flexibility feature allowing a granular, per-interface setting that adjusts the behaviors of each interface in real-time. They are designed to be changeable at any time and for any reason, no matter the operating state of the interface. The IEMs, in order of increasing strictness, are as follows:  
* Address Awareness Disabled (AAD): The interface must disable any generation or verification of addresses during NDAR transactions. It must completely withdraw from any activity related to VBA, with the exception that it can still listen for and capture the current voucher state.
* Address Generation Only (AGO): The VBA generation and process is followed during SLAAC self-assignment, but the interface's address verification shim must be disabled for all NDAR transactions.
* Address Generation and Verification with Levels (AGVL): VBA generation and verification are performed, but any failure to verify a neighbor must not be strictly enforced. Purported LL2 IP bindings which fail to verify are instead tagged in the Neighbor  
Figure 6: Another comprehensive depiction of VBA verification is given. The steps are equal to Figure 5, except step 5 considers the equivalence check its own process. As shown, VBA processes do not modify the typical Neighbor Discovery process or exchange. Instead, software local to each interface, if equipped, will act to verify the received IP addresses during NDAR transactions based on the interface’s selected Enforcement Mode.  
Cache as \"Unsecured\" entries, and those which do successfully verify are tagged as \"Secured\". The Secured responses are always preferred over the Unsecured ones, which permits the legitimate, verified bindings to receive sole connection priority without denying communication to neighbors whose VBAs do not successfully verify (and for which a Secured cache entry does not exist).
* Address Generation and Verification (AGV): Any purported binding which does not verify must be dropped immediately from the Neighbor Cache. Interfaces opting to use this mode will guarantee themselves protection against neighbor spoofing threats because they will only ever be able to transact IPv6 packets with other valid VBAs on the local network. Flexibility with neighbors in mixed networks where some nodes do not have VBA capabilities is consequently revoked.
## 3 Results
### Pain Significantly Decreased after Injection of PRS  
VAS scores presented passed the normality test (\(p>0.05\)) in all cases except for pre-injection data. Differences in means observed were unlikely due to random sampling (RM ANOVA, \(p<0.005\)). Mean pre-injection VAS scores for pain significantly decreased from \(4.83\pm 1.89\) to \(3.05\pm 2.06\) twelve months post-injection ([PERSON]'s multiple comparisons test, \(p<0.01\)). After six months, a small non-significant increase in mean VAS scores for pain was seen as compared to three months post-injection (Figure 4).  
WOMAC pain scores all passed the normality test (\(p>0.05\)). Differences in means observed were unlikely due to random sampling (RM ANOVA, \(p<0.0001\)). Mean pre-injection WOMAC scores for pain significantly improved from \(61.07\pm 11.30\) to \(74.64\pm 15.38\) twelve months' post-injection ([PERSON]'s multiple comparisons test, \(p<0.05\), Figure 5).  
Figure 4: Visual analogue score (VAS) scores for pain of the osteoarthritis knee treated with PRS. A lower VAS score corresponds to less pain. Results are expressed as mean \(\pm\) standard deviation. Only significant results are marked. PRS = platelet rich stroma; * = \(p<0.05\); ** = \(p<0.01\); *** = \(p<0.001\).