esa-sceva/satcom-chunk-collection huggingface dataset:
# Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation
## 2 Robotic palpation with Force Based Approach
### Uncertainty Analysis of Force Measurement  
Measurement uncertainties are inherent characteristic for sensors, in which noise and random drift are usually inevitable. Friction is not considered in this paper as the probe is fixed with the force sensor on the end effector of the robot. Meanwhile, the robot is controlled to move along the Z axis for the force analysis. When a probe or tool is attached to the force sensor, the measured force during the dynamic manipulation task will be influenced by the inertial force and gravity of the probe or tool. Figure 2 illustrates the procedure of robotic palpation, where the probe is controlled by the robot to move along the \(Z\) axis to contact the soft tissue in which several hard inclusions are embedded. The probe is designed with a spherical tip, and during the palpation, the probe is pressed to proper indentation depths on the soft tissue. The palpation is a dynamic process within the \(Z\) axis of the Cartesian space of the robot. We focus on the application of robotic-assisted palpation in open space. The RMIS procedure is not considered in this work, in which the surgical tools are inserted into the patient body through a small hole around 10 cm. Other friction and disturbances are easily caused in RMIS. The measured force from the sensor can be expanded as:  
\[f_{m}=f_{e}+f_{i}+f_{g} \tag{2}\]  
in which the \(f_{m}\) is the measured force from the sensor, \(f_{e}\) is the actual interaction force between probe and the soft tissue (which can be considered to accurately reflect the tissue interaction force), \(f_{i}\) denotes the inertial force during the palpation and \(f_{g}\) is the gravity effect due to the probe attached to the force sensor. Through the analysis of the force measurement uncertainties, it is noticed that simply adopting the measured force from a sensor cannot accurately reflect the interaction force, which may affect the further analysis of the stiffness using YM, thus YM calculated by the measured force from a sensor can be obtained as:  
\[E_{m}=\frac{3f_{m}(1+v)}{8d_{in}\sqrt{rd_{in}}}=\frac{3(f_{e}+f_{i}+f_{g})(1+v )}{8d_{in}\sqrt{rd_{in}}}. \tag{3}\]  
However, the actual YM of the soft tissue should be calculated as:  
\[E_{e}=\frac{3f_{e}(1+v)}{8d_{in}\sqrt{rd_{in}}}. \tag{4}\]  
The force uncertainties indicated as \(f_{i}\) and \(f_{g}\), easily make the obtained YM inaccurate, thus challenging further analysis of robotic palpation.  
Figure 2: Force analysis of robotic palpation: The probe is moved to press the soft tissue phantom, \(A\) is the measured force from the sensor denoted as \(f_{m}\); \(B\) is the actual interaction force between the probe and the environment defined as \(f_{e}\); \(C\) is the palpation depth between the spherical tip of the probe and the surface of the soft tissue phantom as \(d_{in}\).
# Measurements of Room Acoustic and Thermo-Hygrometric Parameters--A Case Study +
## 5 Conclusions  
The main acoustic parameters in a room were examined in this research in relation to how temperature, humidity, and air velocity variations affect them. The statistics and analysis of the data from the reverberation time (T30), the early decay time (EDT), strength (G), definition (D50), clarity (C50 and C80), interaural cross correlation coefficient (IACC), lateral fraction (LF), and lateral efficiency (LE) were the key points of attention. The experimental work reported in this research demonstrated that the variation of acoustic parameters and the variation of temperature and humidity variables are correlated linearly. Data collection and R-based statistical analysis were used to determine the sensitivity of each parameter to temperature and humidity variables at different frequencies. A further research campaign should be undertaken in the winter to confirm the predictive model's accuracy and enable further calibration. Furthermore, while it is possible to deduce that the reliance of acoustic parameters on humidity and temperature should be perceived by people in a room based on JND (just-noticeable difference), the extent to which such observable humidity-temperature changes are perceptible may also be examined further. **Author Contributions:** Conceptualisation, N.G. and L.T. ; methodology, R.Y. and N.G. ; software, N.G. ; validation, N.G. and R.Y. ; formal analysis, R.Y. ; investigation, N.G. and R.Y. ; resources, N.G. and L.T. ; data curation, N.G. and R.Y. ; writing--original draft preparation, R.Y. ; writing--review and editing, R.Y. and N.G. ; supervision, N.G. ; project administration, L.T. and N.G. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was partially funded by the project \"SIPARIO-II Suono: arte Intangibile delle Performing Arts--Ricerca su teatri italiani per l'Opera\" POR-FESR 2014-20 PG/2018/632038. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are unavailable due to privacy reasons. **Acknowledgments:** The authors would like to acknowledge [PERSON] from DICAM of the University of Bologna for supporting this research. **Conflicts of Interest:** The authors declare no conflict of interest.
# Multi-Device to Multi-Device (MD2 MD) Content-Centric Networking Based on Multi-RAT Device
## 2 Related Works and Problems
### D2D Communication  
D2D communication is a technique whereby communication is established directly between devices without having to go through a network infrastructure [1; 2]. As a means of overcoming the overloading of the base station, D2D communication has been attracting the attention of researchers. In the next-generation wireless communication environment, which is expected to support networking between devices, D2D communication will be afforded a much greater level of performance.  
D2D communication makes it possible to reduce the load on the cellular network since it relies on direct communication between devices that are in close proximity. Furthermore, it is possible to improve the utilization of radio resources by reusing radio frequencies and extending cell coverage through relay communication between devices. Also, it is possible to reduce the delay and power consumption that arises during communication between devices [2; 16; 17; 18]. D2D communication not only improves the performance of a cellular network, but also creates communications services such as social networking and location-based services [2; 18].  
As shown in Figure 1, D2D communication can be divided into cellular-network-based D2D communication and autonomous D2D communication. Relative to autonomous D2D communication, cellular-network-based D2D communication is more efficient in terms of the connection time and power consumption [18]. However, autonomous D2D communication can continue to operate in situations in which the cellular network fails, such as in the event of a disaster. Also, it is possible to reduce the overhead of a network and simplify the structure of that network by enabling devices to detect other devices themselves.
## 5 Conclusions
Then modified NN-based DD-LMS is cascaded to improve communication performance by reducing phase offset, making received constellation points more concentrated and closer to standard constellation points. Compared with the benchmark converter, the cascade receiver could achieve 89.6% of the signal Vpp dynamic range with 12.4% of complexity or 94.1% of the signal Vpp dynamic range with 43.5% of complexity in the 64 APSK UVLC system. Furthermore, the ratio of signal Vpp dynamic range and total trainable parameters is up to \(1.24\times 10^{-1}\) mV, while that of the benchmark converter is \(1.95\times 10^{-2}\) mV. The cascaded receiver used in 64 APSK UVLC systems is experimentally validated to achieve enhanced performance between communication performance and computation complexity, regarded as a promising scheme for future high-speed underwater VLC. Conceptualization, X.L. and N.C.; methodology, X.L., F.H. and N.C.; software, X.L. ; validation, X.L. ; formal analysis, X.L. ; investigation, X.L. ; resources, X.L. ; data curation, X.L. ; writing--original draft preparation, X.L. ; writing--review and editing, F.H. and N.C.; visualization, X.L. ; supervision, N.C.; project administration, N.C.; funding acquisition, N.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China (2022 YFB2802803), the Natural Science Foundation of China Project (No. 61925104, No. 62031011, No. 62201157) and Major Key Project of PCL. Not applicable. The data that support the findings of this study are available from the corresponding author upon reasonable request. The authors declare no conflict of interest.
# Numerical Modeling of 3D Slopes with Weak Zones by Random Field and Finite Elements
## 2 Methodology
### Simulation and Model Verification of Weak Zones in Slopes
Figure 8: A typical realization of two-phase random media for a slope with a weak zone fraction equal to 0.5 (the black area represents weak zones, and the gray area represents rock). (**a**) Schematic diagram of a typical 3D slope with weak zones (3D model used in this research); (**b**) schematic diagram of a typical two-dimensional slope with weak zones. safety factor of the 3D model was 1.3% higher than that of the two-dimensional model ([PERSON] et al., 2018) [33]. Considering that the number of Monte-Carlo is 100, the deviation of the mean is basically within the acceptable range. Compared with existing results, it can be verified that the 3D slope method representing the weak zones on the basis of two-phase random media is reasonable. Meanwhile, due to the spatial variability of the material parameters of the slope with weak zones, there is no straightforward rule for the failure mode of the slope and the irregular critical slip surface. The results obtained in this study suggest that the characterization of a 3D slope with weak zones on the basis of two-phase random media, combined with the finite element method, is able to automatically locate the critical slip surface where the internal strength of the slope is insufficient to resist the shear stress. At the same time, it can be seen from Figure 9 that when the slope contains weak zones, the safety factor is 0.64, and when the slope is composed of rocks, the safety factor is 3.2 (the simulation results are consistent with the analytical solution). The median safety factor of slopes with weak zones and rocks is 1.92, which is greater than the safety factor of 1.14 obtained when the volume fraction of weak zones is 50%, meaning that the failure mechanism of slopes with weak zones may be dominated by weak zones. To study the failure principle of slopes with weak zones, Figure 10 shows the plastic strain diagrams of homogeneous slopes and slopes with weak zones. It can be seen from the figure that the homogeneous slope forms an obvious overall shear failure zone. In the slope with weak zones, due to the existence of the weak zones, a series of small shear failure zones appear in the failure mode of the slope along the laminar tendency. These small plastic strain regions reduce the safety factor of the slope. Figure 9: Validation of proposed method by considering a rock-soil slope stability problem.
# Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records
## 5 Implications and Outlook
### Privacy of Synthetic Text  
Privacy/utility trade-off: Our experiments showed that synthetic text does not need to be realistic for utility in downstream NER tasks. This could be exploited to improve the privacy protection. For example, a clearly incoherent combination of names within a document would obfuscate how pieces of information were originally linked. Therefore, future work could investigate how realistic synthetic text needs to be for a given downstream task. Prior work studied the trade-off between perplexity and privacy ([PERSON] et al., 2017), where perplexity is a proxy for utility. This approach could be extended to take utility of synthetic text into account.  
Expanding de-identification: Current approaches to text anonymization mostly define PHI as the 18-categories set out by the HIPAA regulation ([PERSON] et al., 2017). For example, documents in MIMIC-III are shared under the promise that all PHI have been removed and therefore protect privacy sufficiently. However, disregarding whether text was real or synthetic,our user study identified certain aspects of notes which are not covered by automatic PHI extraction methods. Therefore, the common approach to protect privacy in natural language text might have to be re-evaluated and expanded to take, for example, specific descriptions of unusual events into account.  
Embedding privacy: Given the examples of privacy leaks identified in the user study, it seemed that most would have been prevented if the model could not reproduce larger text chunks from a training EHR note. A way to ensure this from a mathematical perspective is to train the generative models with a differential privacy (DP) objective. The premise of DP is that no output could be directly attributed to a single training instance [2; 7; 19; 35]. In this study, we consciously chose not to include DP to maximize the utility of the synthetic corpora for the downstream task, but we recommend that future research uses DP in order to minimize privacy risks.  
Limitations of user study: While our user study provides insights into the privacy of synthetic records, it does not allow us to draw conclusions on the privacy of a synthetic corpus at large. To be able to publish synthetic corpora under the premise that they protect privacy of data subjects, principled ways of measuring the involved privacy risks are needed. Developing these approaches is an important direction for future work.
## 3 Methods
### Training Algorithm
#### 3.1.1 Dividing and Parallelizing Transformer-Based Models  
Transformer-based models are mainly composed of three types of layers: embedding layer, transformer layer, and unembedding layer. The model performs sequential computations in order, starting with one embedding layer, followed by multiple transformer layers and, finally, one unembedding layer. Each layer consists of a large number of parameters, and the count of these parameters can be further expanded using hyperparameters. Additionally, transformer layers can have longer sequence lengths. We manage individual layer types as sub-models, enabling flexible training with variable sequence lengths and focusing on multi-GPU parallelization optimized for individual layers. This makes it possible to respond flexibly to the expansion of layer parameters.  
Each sub-model is composed of large-scale parameters that perform various roles. As the size of the parameters maintained for large-scale training expands, the capacity required to hold the parameters and the size of the intermediate computation results increase, making it impossible to maintain one sub-model within a single GPU memory. To solve this, we employ Tensor Parallelism. Tensor Parallelism equally divides all the parameters that constitute the sub-model among \(m\) GPUs. The set of parameters \(W_{i}\) that constitute the \(i\)-th sub-model is defined as follows:  
\[W_{i}=\{w_{i1},w_{i2},w_{i3},\ldots\}\]  
where \(w_{i1},w_{i2},w_{i3},\ldots\) are evenly divided into \(m\) components corresponding to the row or column of the GPU. Each parameter's divided set is denoted as follows:  
\[w_{ik}=\bigcup_{j=1}^{m}w_{ik}^{j}\]  
where \(w_{ik}^{j}\) is the portion allocated to the \(j\)-th GPU of the \(k\)-th parameter of the \(i\)-th sub-model. Finally, the set of parameters \(W_{ij}\) allocated to the \(j\)-th GPU of the \(i\)-th sub-model is expressed as follows:  
\[W_{ij}=\{w_{i1}^{j},w_{i2}^{j},w_{i3}^{j},\ldots\}\]  
Figure 1 illustrates the way the model is divided and parallelized as described above.  
To apply parallel computation for the evenly divided individual parameters, we utilize the Tensor Parallelism technique used in Megatron-LM [32]. GPUs carry out computations on the divided parameters in parallel across the multi-GPU environment, and the final results can be obtained, identical to those without parallelization, via reduce or gather operations. During this process, communication overhead between GPUs may occur. The DGX-A100 is connected via NVLink and NVSwitch, enabling the rapid transfer of intermediate results generated during the reduce and gather processes, thereby minimizing communication overhead. Therefore, Tensor Parallelism allows for the easy and efficient expansion of the model by simply dividing the existing sub-model parameters and transmitting them to multi-GPU, without altering the original results.
## 4 The GSA in Probabilistic Evaluation of ALSC
### Theory of GSA
#### 4.1.2 The Definition of Kucherenko Index  
The Kucherenko index can be considered as the extension of the Sobol indices for the case, where input variables are correlated. Given an \(m\)-dimension input random vector \(\mathbf{X}=[\mathbf{X}_{v},\,\mathbf{X}_{w}]\), for any subvector \(\mathbf{X}_{v}=\left[\mathbf{X}_{i_{1}},\ldots,\mathbf{X}_{i_{d}}\right]\) and its complementary subvector \(\mathbf{X}_{w}=\left[\mathbf{X}_{i_{d+1}},\ldots,\mathbf{X}_{i_{m}}\right]\), the variance of the output variable \(Y_{s}\) can be decomposed in a manner similar to (17) as follows:  
\[g_{s,0}=E(Y_{s})=\int g_{s}(\mathbf{X})\mathrm{d}\mathbf{X} \tag{20}\]  
Figure 1: Schematic diagram of (**a**) DRPF and (**b**) PRPF.  
\[V(Y_{s})=V_{X_{v}}[E_{\overline{X}_{w}}(Y_{s}|X_{v})]+E_{X_{v}}[V_{\overline{X}_ {w}}(Y_{s}|X_{v})] \tag{21}\]  
\[V_{\mathbf{X}_{v}}[E_{\overline{X}_{w}}(Y_{s}|X_{v})]=\int\limits_{R^{d}}f_{\mathbf{X}_ {v}}(\mathbf{x}_{v})\mathrm{d}\mathbf{x}_{v}\Bigg{[}\int\limits_{R^{m-d}}g_{s}(\mathbf{x}_{v },\overline{\mathbf{x}}_{w})f_{\overline{X}_{w}}(\overline{\mathbf{x}}_{w}\,|\,\mathbf{x}_{ v})\mathrm{d}\overline{\mathbf{x}}_{w}\Bigg{]}^{2}-g_{s,0}^{2} \tag{22}\]  
\[E_{\mathbf{X}_{v}}[V_{\overline{X}_{w}}(Y_{s}|X_{v})]=\int\limits_{R^{d}}f_{\mathbf{X} _{v}}(\mathbf{x}_{v})\mathrm{d}\mathbf{x}_{v}\Bigg{[}\int\limits_{R^{m-d}}(g_{s}(\mathbf{x }_{v},\overline{\mathbf{x}}_{w}))^{2}f_{\overline{X}_{w}}(\overline{\mathbf{x}}_{w}\,| \,\mathbf{x}_{v})\mathrm{d}\overline{\mathbf{x}}_{w}-g_{s,0}^{2}\Bigg{]}^{2} \tag{23}\]  
where \(g_{s}\) denotes the original model; \(\mathbf{x}_{v}\) represents a set of sampling values of the subvector \(\mathbf{X}_{v}\); \(f_{\overline{X}_{v}}(\mathbf{x}_{v})\) is the marginal probability density function of \(\mathbf{X}_{v}\); the complementary subvector under the condition \(\mathbf{X}_{v}=\mathbf{x}_{v}\) denoted as \(\overline{\mathbf{X}}_{w}\), and \(\overline{\mathbf{x}}_{w}\) as a set of sampling values of \(\overline{\mathbf{X}}_{w}\).  
According to the law of total variance shown in (21), [PERSON] et al. defined the following total sensitivity index (hereafter, it will be referred to as the \(K\)-\(ST\) index in this paper) [35]:  
\[K\text{-}ST_{i}=\frac{E_{\mathbf{X}_{\sim i}}[V_{\overline{X}_{i}}(Y_{s}|\mathbf{X}_{ \sim i})]}{V(Y_{s})} \tag{24}\]
# A Novel Global Key-Value Storage System Based on Kinetic Drives +
## 3 Background
### Advantages and Impact of Kinetic Drives  
The above architectural difference shown in Figure 2 is crucial for reducing the amount of data going through the metadata server. Although _keys_ still have to arrive at the metadata server, the actual data (_values_) do not. In key-value store systems, the _value_ size usually is much larger than the size of the _key_. Hence, with the huge amount of key-value pairs in the whole system, the total data traffic going through the metadata server is significantly reduced.  
Furthermore, the traditional KVS system consists of the storage server(s) and multiple hard disks drives, as shown in Figure 2a. Those hard disk drives only store data, without the capability to process the key-value operations by themselves. However, in Figure 2b, this new Kinetic KVS system actually has multiple small key-value stores plus a metadata server. Each Kinetic Drive is a small key-value store and is able to process key-value operations by itself independently. Hence, users can interact with Kinetic Drives in parallel so that the throughput is improved.  
In addition, due to P2P operations, it is easier to migrate data among Kinetic Drives to further improve scalability. The metadata server does not even need to participate in the data transfer between two Kinetic Drives. In a large-scale deployment, this feature becomes especially important because of parallel data transfer among those drives. Therefore, the system can greatly mitigate the bottleneck effect of the server.  
In [29], experimental results showed different write throughput performed by a traditional KVS system and Kinetic Drives when the _value_ sizes were changed, as shown in Figure 3 as an example. We can see that when the _value_ size is small, the traditional LevelDB server indeed has greater throughput. This is because the CPU of the dedicated storage server is more powerful than the CPU of the Kinetic Drive, so that the server processes data quicker. When the _value_ size becomes larger, the amount of data also increases. Due to the internal LevelDB system, data access operations take place inside the Kinetic Drives. Therefore, for Kinetic Drives, those large amounts of data do not need to be frequently accessed by the storage server, so that the I/O traffic can be reduced. However, for the traditional LevelDB server, it still has to access data from/to disk drives, thus generating many I/O operations, which reduce the throughput. Hence, the throughput is greatly increased for Kinetic Drives with a larger _value_ size.