esa-sceva/satcom-chunk-collection huggingface dataset:
In this study, our aim is to improve and test the performance of the membership inference attack [9] method based on neighborhood comparison. Furthermore, we analyze different suffix generation strategies and sorting strategies and study the impact of various methods on the extraction precision of training data, which poses many challenges. First, large language models have multiple suffix generation strategies, with a vast number of possible combinations, significantly increasing the complexity of the work. Second, there are no precedents for utilizing membership inference attacks based on neighborhood comparison for training data extraction in large language models, making it a daunting task to integrate these algorithms into large language models and optimize their performance. Finally, various experimental settings, such as the number of experiments, training equipment, and batch size, also affect the efficiency of training data extraction. Therefore, an efficient training data extraction method requires optimization across multiple aspects. In this paper, our contributions are fourfold:  
* First, this paper improves the membership inference attack algorithm based on neighborhood comparison and integrates it into the challenge of training data extraction, achieving promising results. * Second, this paper comprehensively analyzes the effectiveness of different suffix generation strategies, evaluating them in terms of the diversity and quality of the generated text. Additionally, we examine how variations in generation strategy parameters impact their effectiveness. * Third, this paper analyzes the effectiveness of various membership inference attack methods for extracting training data from large language models. We propose the metric \(E_{mia}\) to better evaluate the extraction rate of membership inference attacks and highlight the inefficiency of these methods in the context of large language models. * Fourth, this paper focuses on achieving a balance between the quality and diversity of the generated text in view of the inefficiency of membership inference attacks. Based on the analysis of the text generated by different strategies, we narrow the range of hyperparameter tuning to efficiently find this balance. Our extraction precision reaches 52.5%, representing a significant improvement over previous work. * Fifth, the training data extraction accuracy for GPT-j-6B achieves an amazing 72.2%. This reveals that the privacy leakage problem of large models becomes more serious as the size of the model increases. The remaining sections of this paper are organized as follows. Section 2 briefly reviews related work. Training data extraction process and various suffix generation and sorting strategies employed in our work are discussed in Section 3. The algorithm for our training data extraction process is presented in detail in Section 4. Section 5 shows the experimental results and provides a detailed analysis, while Section 6 discusses potential defense mechanisms against the training data extraction methods proposed in this paper. Finally, Section 7 concludes the paper.
# Optimal Steady-State Range Prediction Filter for Tracking with LFM Waveforms
## 2 Problem Definition and Conventional Filter Design
### Conventional Filter Design Methods
Although an arbitrary random motion parameter (not only acceleration but also velocity, for example) is assumed for process noise in [9], this study also assumes Kalman filter equations using white-Gaussian process noise. This means that the above conventional design methods are not optimal when other process noise is assumed (including situations where white-Gaussian process noise is not assumed). Moreover, the Kalman filter optimizes the _estimation of the target state_, whereas the _prediction of the range_ is not optimized.
## 5 Experimental Results
### Image Transmission between Two Computers  
For evaluation of the practical application of FM-ACSK transceiver for wireless communication using data exchange protocols, image transfer tests between two independent computers were made. Image is transferred using a very simple proprietary protocol, described in textbook [44]. MATLAB scripts generate, receive and reconstruct data based on the implemented protocol. Additional information on how image frame generation is carried out is described in Appendix A and image reconstruction is described in Appendix B.  
For the testing, the 8-bit greyscale image of size 100 \(\times\) 100 pixels was used. This image contains 800 bits per pixel column which are transmitted in each frame. Each pixel has a value from 0 to 255 that defines its color on the greyscale. Transmission of an image was carried out between two independent computers, each connected to an Adalm PlutoTMSDR. The distance between sites was around 20 m. In both tests, the receiver's sensitivity was set to 30 dB and the transmitter's power to 4 dBm. Image transmission was carried out with and without the use of a (1023, 1013) Hamming code, and in both tests energy-based detector was used. Transmitted and received images are shown in Figure 16.  
Both received images have blank columns that were lost or moved to another column when the protocol header is damaged, and an incorrect sequence number in the header was received. Both images also have incorrectly colored pixels, which can happen due to wrong data bit reception and/or DM.
# Quasi-Passive Resistive Exosuit for Space Activities: Proof of Concept
## 3 Experimental Evaluation
### Subsystems Validation
#### 3.1.2 Lab Validation of IMUs Sensor Technology  
The IMU-based knee angle measurement system has been assessed with a healthy subject walking on a treadmill (3 km/h) and compared against an orthosis equipped with a potentiometer to extract knee angle references [94]. The IMUs were directly mounted on the user's legs and both algorithms (i.e., LSQ and EKF) were run in real-time. Figure 7A,B, show taht the EKF gives very good angle estimate, removing at the same time the drift from the signal. The estimation of the RMSE of the LSQ algorithm is \(7.5^{\circ}\pm 1.8^{\circ}\) after the removal of the drift with the use of the Matlab detrend function. The estimated drift has a linear rate of \(0.65^{\circ}/\)s (after 100 s the measurement is off by about \(65^{\circ}\)). The absolute error of the EKF based estimation is \(5.6^{\circ}\pm 5^{\circ}\) with no drift. During these experiments, the magnitude of the angles measured with the potentiometer differ from those computed with the IMUs. This difference is due to the relative movement between the leg and the exosuit because of the flexibility of the attachments. In conclusion, even if the error is not very low (\(5^{\circ}\) error over a maximum range of motion (RoM) of about \(50^{\circ}\)), the EKF based algorithm allow the use of the IMU technology for the specific scope.
# Evaluating Levels of Automation in Human-Robot Collaboration at Different Workload Levels
## 2 Materials and Methods
### Study Hypotheses  
The model for the study (Figure 5) and the hypotheses describing the proposed connection between the constructs, user preferences and the study variables (LOA and levels of workload) along for the rationale for the hypotheses are presented as follows:  
We suspect that at all workload levels, high LOA will enable the users to perform efficiently and effectively since the high LOA involves the robot carrying out most aspects of the main task which would likely improve performance [37]. Therefore, we propose:  
**Hypothesis 1**.: _Quality of task (QoT) execution will be higher with high LOA than with low LOA for all workload levels._  
Several meta-studies conducted regarding levels of automation [38], ref. [39] seem to suggest that the workload experienced by users is influenced by the LOA of the system, particularly in situations of routine performance. This does not discounttenance the effect of task complexity but seems to point to the effect level of workload may have in low task complexity. Since a major component of usability is the users' perception of the system use [40] along with effectiveness and efficiency, which high LOA will likely increase, we posit:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Workload**} & **Medium Workload 1** & **Medium Workload 2** & **High Workload** \\  & **Task Complexity** & **Secondary Task** & **Medium Workload 2** & **Medium Workload** \\  & & &  
**Hypothesis 2**.: _Usability will be higher with high LOA than with low LOA for all workload levels._  
Research has revealed that as automation increases, workload is expected to decrease, particularly if the automation is properly designed and does not provide new challenges and tasks related to monitoring or other forms of engagement [39]. Moreover, in the design of adjustable robot autonomy in human-robot systems, research shows that as task complexity increases, robot effectiveness is likely to reduce if the robot is operating at higher autonomy [41]. Users seem to intuitively understand that autonomous systems could encounter difficulties in more complex situations with high uncertainty [42] Therefore, in terms of user preferences, we propose:  
**Hypothesis 3**.: _Participants will prefer high LOA to low LOA for high workload and low LOA to high LOA when task complexity is increased._
# Numerical Calculation of Slosh Dissipation
### Problem Description  
The experimental campaign on which simulations are based is detailed in [4]. A tank is connected to a set of six springs: three on the upper and three on the lower side. The rectangular tank has the following size: \(L=10\) cm (length), \(D=6\) cm (height), \(W=6\) cm (depth), while the filling height is denoted \(H\). The tank is filled up to 50% of its volume (i.e., \(H=3\) cm), and water and oil are considered as sloshing liquids (water mass of \(m_{l}=0.18\) kg and oil \(m_{l}=0.162\) kg). This is to study both low and high Reynolds number cases.  
When the springs are released, the tank oscillates at a characteristic frequency of about \(f_{0}=1/T=6.51\) Hz for both the liquids. Figure 1 sketches three pictures taken at different time instants during the experimental test (more details on the experiment can be found in the article by [4]). At time \(t=T\), the tank reaches its maximum vertical displacement \(2A=1.14L\) and a maximum acceleration \(a_{\max}\approx 8.8\) g. The latter value is close to 10 g, which is of the same order of magnitude as limit design cases of an airliner wing during a wind gust (see [2]). The maximum velocity \(U_{\max}\) of about 2.33 m/s is reached by the tank at \(t=0.747\,T\) during the first rising stage. The reference energy adopted is the potential energy (of all fluid in the tank in its initial configuration) when the tank reaches its maximum vertical displacement, i.e., \(\Delta E=m_{l}g\,2A\), which is equal to \(\Delta E_{w}=0.2013\) J for the water and \(\Delta E_{o}=0.1812\) J for the oil. The reference power is \(\Delta P=\Delta E/T\).  
The Reynolds number of the problem is calculated using the maximum tank velocity and length dimension (\(\text{Re}=U_{max}L/\
u\)), where \(\
u\) is the kinematic viscosity of the liquid. The resulting Reynolds numbers are 233,000 for water and 4660 for oil.  
Figure 2 shows the recorded vertical motion of the tank considering both water and oil. In the oil test case, the total amount of dissipated energy is lower compared to water. This counter-intuitive result is, indeed, in agreement with the experimental findings in [24] for a tuned liquid damping system. It is explained that water sloshing flows dissipate energy mostly through energetic breaking wave phenomena. In liquids characterized by higher viscosity, the breaking events are less intense and, therefore, less energy can be dissipated. An analogous result was found in [24; 25], where the authors underline that the higher fragmentation phenomena occurring at higher Reynolds number induces a larger fluid energy dissipation. This was similarly found in [22], where greater fragmentation resulted in greater liquid impact.  
An overview of the respective methods, their mathematical formulations and numerical methods will be provided next, starting with the SPH method.
# Cyclodextrin Polymers as a Promising Drug Carriers for Stabilization of Meropenem Solutions
###### Abstract  
Here we report the development of new drug carriers for meropenem based on the hydroxypropyl-\(\beta\)-cyclodextrinatrin (HPCD) polymers with variable linkers, namely, 1,6-hexamethylenediiscyanate (HMD), citric acid (CA), succinin anhydride (SA). The structures of obtained polyesters and polyurethanes nanoparticles (120-200 nm) were investigated by NMR and FTIR-spectroscopy. The PXRD pattern demonstrated that HPCD polymers form complexes with meropenem (MP), and the majority of MP molecules are encapsulated into a complex. MP's imprinting in the HPCD-HMD polymer matrix lead to an encapsulation efficiency of up to 82%. HPCD-HMD and HPCD-SA polymers increase MP's stability during the storage of its aqueous solution (in 1.4 and 1.2 times, respectively). In contrast, HPCD-CA polymer negatively affects MP's stability. In prospect, the HPCD-HMD polymer may be promising for the development of a highly efficient drug delivery system for MP.  
cyclodextrin polymers; meropenem; drug delivery Article
# An Integrated Decision Support System for Low-Disturbance Surface Mining
## 2 Methodology
### The Framework  
The framework works through a few key criteria (leading indicators) and a manageable number of sub-criteria (sub-indicators), thus enabling the analysis of complex situations [20,21]. Since indicators facilitate decision-making, an indicator-based framework is recognized as an effective tool for conveying information on issues to audiences in a concise and scientifically sound manner [22]. The framework presented in this paper helps to identify how LDM works in practice and to identify areas for focused intervention. This provides a basis for continuous improvement in strengthening the decision-making process in mining companies. The framework consists of three layers, namely, the objectives layer, the criteria layer, and the indicators layer. The goal level is the highest level of representation, i.e., the ultimate goal to be achieved. The goal level represents the top level, i.e., the ultimate goal to be achieved; the criteria level contains the targets that are subordinate to the goal level and serves as the basis for linking individual indicators to the framework; and the indicator level helps to summarize the data and provide meaningful information. Identifying and selecting metrics is not simple; it requires exploring the realities of the surface mining industry and obtaining key information related to LDM.  
At the heart of LDM is the realization of low-disturbance production in open pit mines at the lowest possible environmental cost and low consumption, with reduced land  
Figure 1: The methodological procedure.  
disturbance and rapid reclamation. Therefore, from the perspective of the open pit mining process, the decision analysis of LDM should focus on the following aspects, such as \"Control of stripping works (CS)\", \"Control of emission rock (CE)\", \"Control of pollution emission (CP)\" and \"Control of drainage (CD)\". On this basis, a total of 13 sub-indicators were identified and categorized under 4 leading line indicators, as shown in Figure 2. Below is a description of the leading indicators.
## 3 System Model
### Transmitter Structure  
The transmitter model for both multi-carrier schemes is depicted in Figure 2. The implementation of the multi-carrier modulation scheme is made feasible through FFT and IFFT operations. As per the transmitter structure illustrated, each user's data undergo forward error correction (FEC), channel encoding, broadcasting, and user-specific interleaving to assign a unique identity to each user in a multiple access system. For coding, turbo codes or concatenated convolutional codes (CCC) are employed. According to the authors of [38], two primary types of coding exist in OFDM systems with turbo coding: turbo codes enhance system immunity to noise, while CCCs are utilized in turbo coding concatenation. After this FFT and IFFT operations are performed on a block size of S bits each. For  
Figure 2: Transmitter and receiver structure of multi-carrier multiple access schemes.  
OFDM-IDMA, FFT operation at the transmitter and corresponding IFFT at the receiver is not performed; the rest of the structure remains same as for SC-FDMA-IDMA [19; 20].  
For an individual user k, time-domain-modulated data symbols in a sequence of size S vector can be represented as follows:  
\[\mathrm{x_{k}}=\left[\mathrm{x(0),\;x(1)\ldots x(S-1)}\right]^{\mathrm{T}} \tag{1}\]  
where (J\({}^{\mathrm{T}}\) denotes transpose.  
Time-domain-symbol sequence is frequency-spread using FFT of size S. FFT precoding is performed for SC-FDMA-IDMA signal only. This transformation results in a low PAPR of the SC-FDMA-IDMA system. Hence, each user of an SC-FDMA-IDMA or OFDM-IDMA system occupies S number of sub-carriers. Data symbols in the frequency domain can be represented as  
\[\mathrm{x_{k}}=\left[\mathrm{X(0),X(1)\ldots X(S-1)}\right]^{\mathrm{T}} \tag{2}\]  
If N > S is the total number of orthogonal sub-carriers considered in a multi-carrier IDMA system design, then out of these N, S frequency domain data symbols are assigned to the S orthogonal sub-carriers for each user.  
In these multi-carrier IDMA schemes, overlapped sub-carriers are allowed among users, since distinguished interleavers differentiate users in a multiple access system. Different orthogonal interleavers have been suggested in the literature to distinguish the users; here, we have implemented IDMA with the PPI interleaver suggested in [26]. Simple implementation complexity for the MUD and the least memory requirement is the reason for selecting the PPI interleaver to distinguish the users.
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 6 Theoretical Performance Bounds with Simple Models
### Validation of the Theoretical Bounds  
In this subsection, simulations are performed to validate the theoretical bounds derived above. The parameters are set as follows: the user interest follows a uniform distribution in \([1,10]\); the file size follows a uniform distribution in \([1,50]\); the number of users \(U=10\); the number of contents \(F=500\); the length of the recommended list for each user \(N=50\); and the system capacity is 188.  
Figure 4 shows the total user interest as a function of system outage when different algorithms proposed in Section 4 are applied. The performance tradeoff curves are obtained by adjusting the value of parameter \(\sigma\) in the algorithms. The theoretical bounds are also calculated and shown. The upper limit of interest is shown to be 4771 according to Equation (36); the upper limit of outage is shown to be 0.9306 according to Equation (40); and the upper limit of interest at zero outage is 4373. The point at (0.9306, 4771) shows the performance benchmark of traditional recommendation algorithm, in which both the user interest and transmission outage reach the maximum. We can see that the three theoretical  
Figure 3: Comparison of the empirical value and the numerical value of the upper limit of outage, where \(U(x,y)\) represents a uniform distribution in \([x,y]\).  
bounds derived above form a square area, which well characterizes the performance tradeoff region of the proposed algorithms.
## 3 System Design
### Discuss  
From the above descriptions, we can summarize that CCSM has the following characteristics.  
* CCSM is based on the UIN architecture and inherits the characteristics of UIN in terms of mobility, security and reliability, which means that CCSM can support endogenetic security and mobile multicast.
* CCSM separates the multicast routing computation and multicast data forwarding, which simplifies the multicast routing function on router. Therefore, CCSM is suitable for the scenarios where computing and storage resources are constrained such as IoT applications.
* CCSM provides the multicast member registration mechanism which supports the multicast source and receiver access control, and provides a publish/subscribe model for multicast services.
* CCSM provides the multicast join/leave mechanism which is different from the traditional IGMP/MLD, and it supports the controllable multicast membership management.
* CCSM adopts the centralized multicast routing computation which can easily support various multicast policies including security and QoS, and supports controllable multicast routing, and speeds up the multicast routing convergence.
* CCSM has lots of potential applications due to the existing UIN applications in vehicular communication [13; 14; 15], satellite-terrestrial networks [16], wireless sensor networks [17] and smart grid [18]. To provide a larger scale deployment of CCSM, some function entities can be implemented in an overlay mode. To be more specific, ASR can be deployed in form of a home router close to the multicast members. The CR can inherit the existing routers. IDMS, AC and MMC can be deployed in the cloud platform.
# A Case Study on the Design and Implementation of a Platform for Hand Rehabilitation
## 2 Hand Rehabilitation Background  
There are different physical disabilities [24]. In general, these are conditions that affect a person's mobility, stamina, or dexterity. There are many reasons for physical disability, like brain or spinal cord injuries, multiple sclerosis, cerebral palsy [25], and many more. Some may affect, either temporarily or permanently, a person's life. Examples of short term disabilities may include broken limbs, injuries, disabilities connected with surgery, medical treatments, or severe diseases. These patients experience various movement problems, and one of the main challenges is to reduce disability and raise their quality of life as quickly as possible. This process requires rehabilitation services to overcome these problems.  
To improve strength, movement, or flexibility, disabled people need help from different therapists. Specific disabilities need special exercises in a clinical setting and experts' involvement (therapists, physiotherapists, occupational therapists, doctors, and others). In our paper, we address them superficially as therapists or medical staff. Each day millions of people with movement problems visit rehabilitation centers. Due to limited medical resources, patients need to wait for several weeks or months to start treatment [26]. Every delay in the start of rehabilitation has a negative impact on the rehabilitation result and rehabilitation time/cost. The advance of rehabilitation services [27] is one of the key factors in addressing the challenges of reducing waiting queues in rehabilitation centers worldwide.  
Suppose we take an example of a hand and wrist temporary disability, which may happen due to surgery, fracture, dislocation, and so forth. Physiotherapy treatment starts with motion assessment of the hand, wrist, and fingers. Upon assessing finger flexion and extension deviation, therapists decide on a specific physical activity and provide exercises for them. Some exercises include training with therapists (passive range of motion); some include individual training (active range of motion). These exercises strengthen muscles, ligaments, and tendons around the joint, generate more power, or increase endurance. These exercises are helpful, for instance, for inflammation of the tendons.  
The RehabHand platform addresses hand and finger individual training. There are two main user scenarios:  
1. A therapist prepares customized training for the individual patient, and supervise them occasionally in rehabilitation facilities.
2. Individual training implemented at home or work sites with remotely guided and controlled rehabilitation.  
Figure 1: Comparison of RehabHand platform with traditional development and relationship between hardware and software.  
During the design and implementation of the rehabilitation platform, we had these user scenarios in our minds.