esa-sceva/satcom-chunk-collection huggingface dataset:
# Steering the Clinical Translation of Delivery Systems for Drugs and Health Products
## 2 Lectures  
### Session A: How Informatics Can Support Drug Delivery Technologies  
#### 2.1.1 Modeling and Simulation, a Powerful Tool to Inform Drug Development:  
A First-Time-in-Human Study Design in Oncology  
[PERSON]  
Department of Electrical, Computer and Biomedical Engineering, Universita degli Studi di Pavia, 27100 Pavia, Italy; [EMAIL_ADDRESS]  
Mathematical modeling and computer simulations are a powerful tool useful in all the phases of drug development. Mathematical modeling plays a central role in knowledge elicitation and in making explicit all assumptions; computer simulations allow exploration of different scenarios before performing experiments, helping also in designing them and limiting the trial-and-error approach typical of experimental and observational sciences. In this talk, as an example, modeling and simulation is applied to support a first-time-in-human study design in oncology. Data from pharmacokinetic and toxicokinetic studies in monkey and in vivo tumor inhibition growth experiments in xenograft mice were exploited to inform this step by using in the pre-clinical species modeling approaches that are considered reference in the field and then translating them to human [1; 2].  
First, pre-clinical data were modeled: (i) a 2-compartment population model with linear and saturable elimination from the central compartment was adopted to describe pharmacokinetic (PK) data in monkey; (ii) myelosuppression in monkey was well modeled by the population Friberg model [3] that adequately described the observed neutropenia after single and repeat doses; (iii) antitumor drug efficacy was assessed in xenograft mice by the Simeoni TGI model [4], from which the minimal \"effective\" concentration for tumor eradication was derived [5].  
Then, pre-clinical models were translated to human under clearly defined hypothesis. PK in human was translated from the monkey model by applying the allometric scaling approach; distribution volume and both linear and non-linear clearance were scaled based on body weight using an allometric exponent of 1. Myelosuppression in human caused by the anticancer treatment was predicted by translating the Friberg model previously identified in monkey: time-courses of neutropenia in patients were predicted based on expected PK in human, typical human system-related parameters reported in the literature and drug-effect parameters estimated in monkey [6]. The target exposure in human was obtained from the minimal \"effective\" concentration derived in mice. The risk-benefit profile of different dosing schedules was evaluated based on predicted neutropenia and expected antitumor activity in patients.  
Finally, clinical trial simulations were performed to explore plausible scenarios for the FIH study, enabling the computation of several quantities of interest that supported, for example, the feasibility of the proposed accelerated titration design with 100% dose escalations that reverts to a more conservative design with smaller dose escalations once Grade \(\geq\)2 toxicity is observed.
## 3 Results  
### Validation  
The models were validated by comparing their calculated lattice constants with established reference data [14; 36; 37; 38]. The lattice constants for both the analytical Buckingham potential model and the machine learning-based DP models were determined by measuring the dimensions of the equilibrated systems. The calculated lattice constants are presented in Figure 5, with a detailed comparison summarized in Table 4.  
Our results demonstrate good agreement with previously reported values, confirming the reliability of the interatomic potentials employed in this study. Notably, in the Buckingham potential model, the lattice constants exhibit a slight decrease as lithium content is reduced, consistent with trends observed in earlier studies with other cathode materials [39; 40; 41]. This behavior aligns with the expected structural response to lithium deintercalation, where the reduced lithium content leads to contraction of the crystal structure due to diminished Coulombic repulsion and bonding rearrangements.
## 4 Results  
Following the measurement study in the Sorrentina peninsula area, 70 values of the average yearly concentrations of Rn in dwellings at 100 measured locations were selected for the analyses. Figure 3 depicts the geographical distribution of the measurement points (circles) and the relative yearly radon concentration value (different colors). The entire investigation was carried out in accordance with an established quality assurance program, and the results acquired by the Lab.RAD laboratory during the comparison met the BfS requirements. The main findings concern the arithmetic mean (AM), geometric mean (GM), standard deviation (SD), coefficient of variation (CV), geometric standard deviation (GSD), minimum (Min), median (Med), and maximum (Max) for the total measurements on the Sorrentina peninsula, which are reported in Tables 1 and 2, before and after data normalization, respectively. For the monitored dwellings, the conversion factors are \(K_{-1\to 0}=0.91;\ K_{1\to 0}=1.24;\ K_{2\to 0}=1.29\). Tables 1 and 2, and the conversion factor data, allow us to determine how the floor affects the radon concentration values. The AM and GM values following data normalization are greater in relation to the floor than the non-normalized data. Figure 3: Indoor radon concentrations on the Sorrentina peninsula. The trend of the distribution of indoor radon concentration values is commonly recognized in the literature to be of the log-normal type. Figure 4a indicates the frequency distribution of the annual indoor radon concentration for the 70 dwellings, and Figure 4b indicates the distribution trend assessed for this study, which matches the expected trend according to the Kolmogorov-Smirnov test (\(p>0.05\), 95% confidence level) and the Shapiro-Wilk test (\(p\)-value = 0.2). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\begin{array}{c}\textbf{Statistical}\\ \textbf{Parameters}\end{array}\) & \begin{tabular}{c} **Sorrentina** \\ **Peninsula** \\ \end{tabular} & \begin{tabular}{c} **Massa** \\ **Lubrene** \\ \end{tabular} & \begin{tabular}{c} **Meta** \\ **Sorrento** \\ \end{tabular} & \begin{tabular}{c} **Piano di** \\ **Sorrento** \\ \end{tabular} & \
\begin{tabular}{c} **Sant\({}^{\prime}\)Agnello** \\ **Sorrento** \\ \end{tabular} &
\begin{tabular}{c} **Sorrento** \\ **Equense** \\ \end{tabular} \\ \hline AM (Bq/m3) & 156 & 179 & 152 & 141 & 216 & 178 & 104 \\ SD (Bq/m3) & 135 & 199 & 97 & 124 & 172 & 152 & 47 \\ CV = SD/AM & 0.87 & 1.11 & 0.64 & 0.88 & 0.80 & 0.85 & 0.45 \\ GM (Bq/m3) & 120 & 131 & 127 & 109 & 160 & 133 & 92 \\ GSD (Bq/m3) & 1.35 & 1.38 & 1.31 & 1.36 & 1.44 & 1.41 & 1.26 \\ Min (Bq/m3) & 25 & 56 & 50 & 42 & 44 & 52 & 25 \\ Med (Bq/m3) & 108 & 107 & 137 & 120 & 113 & 132 & 100 \\ Max (Bq/m3) & 722 & 722 & 351 & 513 & 492 & 546 & 204 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical parameters both for the total measurements on the Sorrentina peninsula and for each municipality before data normalization.
## 5 Results and Discussions
### Effect of Optimizers with Varying Network Sizes on Cats and Dogs  
The results obtained for the Cats and Dogs dataset are reported in Table 8. ADAM, in combination with the depth/width CNN model, achieved the best performance in terms of validation accuracy across the three CNN architectures at 91.1%, followed closely by ADAM(88.9%), RMSProp (87.7%) and NADAM (86.5%), although with variations for convergence time and loss. The other remaining optimizers did not have any significant performance improvements when compared to the simple CNN, except marginal improvements with validation convergence time for NADAM on depth CNN model and better validation loss for RSMSProp on depth/with CNN. The overall worst performers were ADADelta and vanilla SGD. Figure 1 shows plots of train and validation test accuracy and loss against epoch on the best-performing configuration with ADAM and increasing CNN depth/width. More details of all the experimental plots for CNN with varying network sizes for Cats and Dogs are reported in Figure A1 of Appendix A.1, Figure A5 of Appendix B.1 and Figure A7 of Appendix C.1. We noticed no significant improvement after about 500 epochs.
It is known that a PD controller can guarantee a semi global asymptotic stability after appropriately tuning the gains [23; 24]. Several studies have been conducted to modify the linear PID controller that can guarantee an asymptotic stability. For example, PD control with sliding mode compensation [25], PD-based fuzzy sliding mode control [1], PD control with neural compensation [22] and so on. It is well understood that the PD controller can guarantee the stability for the robotic manipulators, but the asymptotic stability cannot be achieved if the robot dynamic contains gravitational torque. The exoskeleton presented in this study is designed to safely support the user in their ADL, especially the C-ring mechanism designed for shoulder and wrist rotation and the worm gear used to drive the elbow joint exoskeleton to hold the output position without energy consumption because of its non-backdrivability [26]. Moreover, hard constraints in the joint mechanisms may not allow the users to move beyond the safety limits. In this paper, we present a PD control in the joint space to control the four degrees of freedom (DOF) upper limb exoskeleton robot [2] and investigate its effect as an assistive device to support individuals with physical impairments of the upper limbs in a set of ADLs. The contribution of the article can be summarized as follows. 1. The proposed design can support the human upper limb musculoskeletal structure in basic ADL by providing a kinematically safe and singularity-free workspace. The deign along with the PD control is able to provide a satisfactory tracking performance. It is hypothesized that the trajectory tracking for C-ring mechanism and worm gear mechanism is less prone to the variation in payload, weight of human arm, and exoskeleton due to its ability to hold the output position without energy consumption. The integration of the upper limb exoskeleton with the CarbonHand glove (BioServo Technologies AB, Kista, Sweden) offers a new paradigm that not only supports the user in manipulation but facilitates them also in hand opening and closing. The experimental evaluation has shown that the proposed design with the PD control scheme is appropriate in performing several ADLs, such as eating/drinking. The paper is organized as follows. The mechanical design of a wheelchair exoskeleton is presented in Section 2 together with the kinematic modeling required to fulfil the task requirements in operational space. The dynamic model of the upper limb exoskeleton along with the PD control scheme is presented in Section 3. Moreover, the PD controller implementation along with the experimental results on the wheelchair exoskeletons is illustrated in Section 4. Subsequently, a discussion on the exoskeleton performance and its potential future directions are presented in Section 5. The work is finally concluded in Section 6.
## 2 Aircraft Wing Load Tests  
### Fundamentals of Strain Method Load Testing  
Currently, resistive strain gauges represent the most cost-effective, reliable, and prevalent methodology for aircraft load measurement. These gauges offer an extensive measurement range, high precision and sensitivity, and favorable frequency response characteristics. The strain gauges are first required to be fixed at critical locations on essential structural components of the aircraft under evaluation--such as the fuselage, wings, landing gear, and tail. These gauges are configured into a Wheatstone Bridge arrangement to facilitate accurate load measurement. Ground load calibration tests are employed to develop matrix equations that delineate the relationship between applied loads and the structural components being tested. These equations enable the analysis of the correlation between the strain gauge bridge responses and the component loads being measured. These include the bending moment, shear force, and torque acting on the wing; bending moment and torque of the fuselage; bending moment, shear force, and torque of the tailplane and vertical tail; and chained moment of the movable surface. Additionally, this study considers the loads in three directions exerted on the aircraft's landing gear. These parameters are determined through strain gauge bridge responses measured in flight and are influenced by relevant flight parameters, among other factors.  
Load spectrum data acquisition is conducted utilizing electrical strain measurement techniques to assess the loads on wing structural components. This process involves selecting critical areas of the aircraft wing structure and employing a strategically designed arrangement of strain gauges and bridge design, where strain signal acquisition can be carried out directly. Overall, many test locations were determined, and the specific test loading locations for aircraft wing loading acquisitions are shown in Figures 1-4.
## 5 Computational Experiments
### Genetic Algorithm (Ga)
#### 5.4.1 GA Pseudocode  
To better understand the GA, we present the additional notation in Table 6 together with the corresponding pseudocode (see Algorithm 1).  
\begin{table}
\begin{tabular}{l} \hline
**GA Additional Notation** \\ \hline _PGA:_ population in Genetic Algorithm. \\ \(i\) initial individuals. \\ \(j\): next generation individuals. \\ \(G_{d,p,DC}\): this gene corresponds to the date of the initial production for product \(p\). \\ \(G_{d,p,DC}^{\prime}\): this gene corresponds to the date of the final transport arrival required to meet the demand for product \(p\). \\ \hline \end{tabular}
\end{table}
Table 6: GA additional notation.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \multirow{2}{*}{\(\boldsymbol{P}\)} & \multicolumn{2}{c}{\(\boldsymbol{f_{1}}\)} & \multicolumn{2}{c}{\(\boldsymbol{f_{2}}\)} & \multicolumn{2}{c}{\(\boldsymbol{f_{3}}\)} & \multicolumn{2}{c}{\(\boldsymbol{f_{4}}\)} & \multicolumn{2}{c}{**DC (\(\boldsymbol{f_{5}}\))**} \\ \cline{2-10}  & **Stock** & \(\boldsymbol{d}\) & & \(\boldsymbol{d}\) & **Stock** & \(\boldsymbol{d}\) & **Stock** & \(\boldsymbol{d}\) & **Stock** & \(\boldsymbol{d}\) \\ \hline \(p_{1}\) & 61.7000 & 62 & - & - & 62.2040 & 62 & - & - & 424.7360 & 44 \\ \(p_{2}\) & 24.8400 & 63 & - & - & 4.6800 & 63 & - & - & 206.1000 & 65 \\ \(p_{3}\) & 0 & 12 & - & - & - & - & 40.0000 & 14 & 300.0000 & 10 \\ \(p_{4}\) & - & - & - & - & - & - & 0 & 15 & 240.0000 & 16 \\ \(p_{5}\) & - & - & 5.1200 & 37 & - & - & - & - & 138.8800 & 39 \\ \(p_{6}\) & - & - & - & - & - & - & 0 & 40 & 420.0000 & 41 \\ \hline \end{tabular}
\end{table}
Table 5: Product stock remaining in dairy plants, in tons.  
```
1:Input: demands \(DM_{p}\), initial stocks \(IQ_{p,p}\), production capacities of each dairy plant \(CF_{f,p}\), vehicle capacities \(CK_{k,p}\), quarantine times \(TQ_{p}\) and stocks in the distribution center \(I_{DC,p,d}\).
2: Initialize \(PGA\) with random individuals representing the delivery days for each product.
3:while (number of generations \(\leq 20\)) do
4:for (each individual \(i\) in \(PGA\)) do
5: Calculate production, quarantine and transport phases of \(i\) using the function 'calculate_phases'.
6: Assess the feasibility of \(i\) using the function 'is_feasible', applying penalties if necessary.
7: Compute \(Tmax\) of \(i\) considering the time to fulfill the demand at DC.
8:endfor
9: Select the individuals for crossover.
10: Apply Two-point crossover in 80% of selected individuals.
11: Apply mutation in the individuals of the new population with a probability of 30%.
12:for (each individual \(j\) of new population) do
13: Reevaluate phases of \(j\) with function 'calculate_phases'
14: Check and correct precedence between products with'repair_individual'
15: Evaluate \(j\).
16:endfor
17: Update population.
18:endwhile
19: Identify the individual with the lowest \(Tmax\) (best fitness) between generations.
20:Output: best individual.
```  
**Algorithm 1** GA for the case study in dairy plants
## 7 Challenges and Limitations
### Data Privacy and Security  
As AI-enabled networks process vast amounts of user data, privacy and security concerns are paramount. AI models, particularly those based on deep learning, require large datasets, often containing sensitive personal information such as communication patterns, geolocation, and usage behaviors. The use of these models without proper privacy controls may lead to significant risks, such as unauthorized access to user data or exposure of private communications [136].  
A key challenge in this area is ensuring data anonymization and encryption during the training of AI models. Traditional encryption methods may not be well suited to  
Figure 12: AI-driven network security in cloud-based communications.  
the computational needs of AI models. Recent techniques like federated learning aim to address this issue by allowing data to remain on the device, with only model updates being shared. However, federated learning introduces challenges regarding the synchronization of models across different devices, potential data poisoning, and ensuring that data remain unexploited [136].  
A key trade-off between privacy protection and model performance can be seen in the following Table 10:
## 3 Challenges and the State of the Art Progresses
### Architectural Choices behind Performance and Security Concer
#### 3.3.4 Transactional Stores vs. Non-Transactional Stores  
Blockchain often uses a transaction model to make a record. A transaction is a state transition that changes data in the blockchain from one value to another. These data can be any type of data tracked in the blockchain, including a cryptocurrency amount, IoT sensor readings, etc. A transaction includes the address of the sender, the address of the receiver, and the exchange between the two parties. To store the custom data on the blockchain, we need to package data into transactions to be able to store it. Some blockchain allows custom data to be appended to a transaction within their protocols, which are called transactional stores.  
Some blockchain does not have that feature. Hence, custom data have to be encoded (if necessary) and used as a receiver's address. A transaction is sent to this specific address and the data are stored in the blockchain. By the non-transactional stores, data are encoded into the receiving address instead of storing in the payload field of the transaction. There are two drawbacks for the non-transactional stores. First, since the address size is limited, the number of data that can be stored is very small. Secondly, the transaction is sent to an address not owned by the sender; thus, the transaction fee is lost.
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 2 Theoretical Review
### Ad-Hoc Networks Routing Protocols
#### 2.2.2 Optimized Link State Routing (OLSR)  
The OLSR [52] is a proactive protocol commonly used in ad-hoc networks. The OLSR uses two control messages for topology discovery and maintenance: Hello and Topology Control (TC). Hello messages are used for neighbor discovery. The TC messages are used to disseminate information about neighbors and the state of the links established between them in order to build the network topology.  
The OLSR employs a technique called Multi-Point Replaying (MPR) to reduce overhead caused by sending control messages and the number of rebroadcasting nodes [53]. This technique is to limit the number of neighbors that can relay control messages. For this to occur each node selects a number of neighbors that can relay the messages. Unselected neighbors receive the messages but do not forward them to other nodes. Additionally,TC packets include a sequence number to avoid infinite retransmissions due to undesirable loops.  
The OLSR is used as a reference in this work because it is a widely used protocol in ad-hoc networks. In addition, this protocol provides better results, in terms of Quality of Service, than other routing algorithms [54] also considering the VoIP service [55].
###### Abstract  
Monitoring induced vibrations caused by blasting works is becoming an increasingly common form of preventive activity conducted in open-pit mines. Measurement stations also record other events unrelated to blasting works. This article presents a comparison of the intensity of vibrations induced by blasting works in an open-pit mine and mining tremors in an underground mine. The recorded data and conducted analyses of vibration intensity and frequency structure also allowed for a comparison of the impact of vibrations on a building structure. Calculations and analyses, conducted in accordance with the procedures provided in the standard PN-B-02170:2016-12 and the rules for applying the Mining Seismic Intensity Scale MSIS-2017, demonstrated a stronger impact on the building from induced vibrations in an underground mine located 10 km away compared to vibrations induced by blasting operations conducted in an open-pit mine, which is approximately 600 m away from the building. The presented material constitutes a unique set of data that can be used to introduce any necessary corrections in the methodology of analyzing vibrations regarding their harmfulness to building structures. The velocity value of vibrations correlated with frequency alone, without taking into account the vibration duration, can lead to incorrect interpretation.  
vibration monitoring; blasting works; impacts of vibration; prediction; assessment of impact using PN-B-02170:2016-12 and MSIS-2017
# TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction
## 4 Dataset and Experimental Set-Up
### PeMS (Caltrans Performance Measurement System)  
Caltrans is composed of five-minute interval traffic data on various freeways. It contains data such as the vehicle flow, speed, occupancy, the ID of the Vehicle Detector Station (VDS), etc. Caltrans dataset has been used in [9; 15; 19] for traffic flow prediction. According to the current state of the art methodologies, traffic data from 1 January 2016 to 29 February 2016 have been aggregated, in this work, every 5 min and then used as a training set, traffic data of March 2016 has been aggregated every 5 and used for the test. The lag of the sliding window has been set to 5 as in [9] for comparison aims. SAEs, LST, GRU and TrafficWave have been tuned using Adam optimizer [30]. All algorithms have been trained for 500 epochs with a batch size of 64.
## 2 Materials and Methods
### Object Detection Model  
#### 2.2.1 YOLO Series Model  
YOLO (You Only Look Once) is a deep learning-based single-stage object detection model widely adopted for real-time detection tasks due to its high efficiency. Unlike traditional two-stage methods such as Faster R-CNN, YOLO formulates object detection as a regression problem, simultaneously localizing object positions and classifying categories within a single forward pass. This approach significantly enhances detection speed and efficiency. Due to its outstanding real-time performance, YOLO has been extensively applied in agriculture for tasks including crop detection and classification, pest and disease monitoring, and farmland management [20]. Since the introduction of YOLOv1 in 2015 [21], the YOLO series has undergone multiple iterations and optimizations, achieving continuous improvements in detection accuracy, inference speed, and model architecture.  
YOLOv11 introduces a scalable lightweight backbone network and an enhanced feature fusion module, effectively improving feature extraction and multi-scale information integration. While maintaining high detection accuracy, YOLOv11 significantly increases inference speed, thereby enhancing real-time performance. The overall architecture of YOLOv11 is illustrated in Figure 3. With its efficient design, strong generalization capability, and low computational overhead, YOLOv11 demonstrates broad applicability and practical value.