esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mean Muscle Activities  
As shown in Table 1, in the conventional carriage, the mean muscle activity of knee extensors RF, VM, and VL increased almost linearly with increasing of loads. The R\({}^{2}\) were 0.917, 0.992, and 0.991, respectively. However, there was no obvious change for knee flexor BF and ST. Additionally, the R\({}^{2}\) were 0.829 and 0.562, respectively. Relating to the load level, the changes of the planter flexor PL is linear.  
However, in Table 2, the with-Exo conditions, RF, VM, and VL activities are not increasing with load level, as shown in Table 2. Some muscle's mean activities even decrease with heavier load level, such as the knee extensor muscles RF and VM. The PL muscle activities are far higher than the other muscles.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Muscles**} & \multicolumn{4}{c}{**Load (Mean Activities)**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**_p_**} \\ \cline{2-2} \cline{5-8}  & **0 kg** & & & & & & \\ \hline RF & 7.315 & 8.265 & 11.16 & 16 & **22.15** & _0.917_ & _0.042_ \\ VM & 10.02 & 13.95 & 18.5 & 23.9 & **393.00** & _0.992_ & _0.003_ \\ VL & 18.65 & 22.25 & 28.15 & 33.3 & **233.17** & _0.991_ & _0.004_ \\ BF & 4.45 & 4.35 & 4.94 & 5.215 & 9.71 & 0.829 & 0.09 \\ ST & 6.61 & 4.755 & 5.41 & 4.645 & 2.56 & 0.562 & 0.25 \\ PL & 7.675 & 8.935 & 10.28 & 14.4 & _19.24_ & _0.906_ & _0.048_ \\ GL & 5.045 & 4.155 & 5.535 & 7.49 & 3.51 & 0.637 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean Muscle Activities During 0 kg, 15 kg, 30 kg, and 45 kg Load Levels Without the Exoskeleton. (**Note:** The determination coefficient R\({}^{2}\) and the statistics [PERSON] used to confirm the linearity in whole cases are shown. The bold and italic style indicates linearity.)  
Figure 7: Time normalized EMG gait patterns for the measured right leg muscle groups during with-Exo and without-Exo walking in the case of 45 kg load carriage.  
Most of the muscles' mean activities increased when using the assistive exoskeletons, especially in the planter flexor PL (\(p<0.001\)), as shown in Figure 8. As far as knee flexors BF and ST were concerned, although the mean activities are higher than the conventional condition, there were no obvious change to the muscle activities with increasing loads. According to the experiment, it could be concluded that the flex process was seriously affected by the exoskeleton. For the plantar flexor muscles PL and GL, much more activity was needed when using the EXO, especially for PL. It may be caused by the exoskeleton self-weight and adaptability.
# Narrative Review on Methods of Activating Irrigation Liquids for Root Canal Treatment
###### Abstract  
As indicated by standards of modern dentistry, the essence of endodontic treatment is chemo-mechanical disinfection of the root canal system. The vital element of this process is the irrigation of canals with designated solutions as well as activating them. This review article summarizes various techniques of activation of irrigants for endodontic treatment such as manual activation, thermal, pressure, sonic, ultrasonic, and laser techniques in order to compare them. Results were gathered using PubMed, Scopus, Web of Science, and Google Scholar databases by searching keywords: activation, irrigation, irrigation protocols, and sodium hypochlorite.  
Activation techniques; endodontics; irrigation; root canals; root canal treatment 1  
[PERSON][PERSON], W.:[PERSON], [PERSON]:[PERSON], [PERSON]:Nararative Review on Methods of Activating Irrigation Liquids for Root Canal Treatment  
1  
M.[PERSON][PERSON], W.:[PERSON], [PERSON]:[PERSON], [PERSON]:Narative Review on Methods of Activating Irrigation Liquids for Root Canal Treatment
Among methods of activation of irrigants utilized during an endodontic treatment we can outline:  
* Manual activation;
* Thermal techniques (internal or external to the root);
* Pressure techniques (EndoVac, Kerr Endodontics, Gilbert, AZ, USA; Rinsendo, Durr Dental, Bietgheim, Germany);
* Sonic/ultrasound techniques (EndoAcivator, Dentsply Maillefer, Ballaigues, Switzerland; EDDY, VDW, Munchen, Germany; Ripssonic, Medidenta International Inc., Woodside, NY, USA; SAF, ReDent, Ra'anana, Israel);
* Passive Ultrasonic Irrigation (PUI);
* Laser techniques.  
The schematic division of irrigation activation techniques is presented in Figure 1.
# Deep Error-Correcting Output Codes
## 1 Introduction
We have conducted extensive experiments to compare DeepECOCs with traditional ECOC, feature learning and deep learning algorithms. The results on 16 UCI machine learning data sets, the USPS, MNIST, CMU motion capture and CIFAR-10 data sets demonstrate that DeepECOCs perform not only better than traditional ECOC and feature learning algorithms, but are also related to deep learning models, in most cases. The contributions of this work are highlighted as follows. * We integrate an online learning method into the ECOC coding, which improves the efficiency of ECOC, especially for large-scale applications. * We employ ECOC as building blocks of deep networks, which sufficiently utilizes the available label information of data and improves the effectiveness and efficiency of previous deep learning algorithms. * We propose the DeepECOCs model, which combines the ideas of ensemble learning, online learning and deep learning. The rest of this paper is organized as follows. In Section 2, we provide a brief review of related work. In Section 3, we present the proposed model, DeepECOCs, in detail. The experimental results are reported in Section 4, while Section 5 concludes this paper with remarks.
# Optimized Deep Neural Networks for Real-Time Object Classification on Embedded GPUs +
## 4 Optimized Data Transfers Scheme for GPU-Only ConvMM
### Pinned Memory Based Allocations  
For CUDA-based realized layers, input images or data must be transferred from the host (CPU) memory to the device (GPU) memory, and the final results must be copied back to the CPU memory from the GPU memory. These transfers can occur several times depending on the architecture of a deep classifier and may affect the overall performance of the system. By default, host allocations are pageable and the GPU cannot copy data directly from the pageable host memory. So whenever a data transfer is invoked, CUDA driver must allocate and copy host data into the pinned (page-locked) memory from the pageable memory and then transfer it to the device memory via PCI-express (Peripheral Component Interconnect-Express) bus as shown in Figure 4.  
The cost of this extra transfer between the pageable and the pinned memories can be avoided by directly allocating the host data in the pinned memory as shown in Figure 5. CUDA provides the feature of allocating the input data into the pinned memory.  
Use of pinned memory is a common practice in CUDA-based acceleration. However, there are some constraints to be considered before using the pinned memory specifically on an embedded platform (like size of trainable parameters to be imported). Pinned memory should not be over-allocated because this can reduce the amount of physical memory (Random Access Memory (RAM)) to the other programs and operating system, which can reduce the overall performance of the  
Figure 4: Pageable and pinned memories on the host side.  
Figure 5: Pinned data transfer.  
used system. Hence, the storage capacity of an embedded device is limited, so use of pinned memory can affect the performance of deep classifiers.
# Involvement of the Orexinergic System in Cancer: Antitumor Strategies and Future Perspectives
## 3 Orexins and Cancer
### Adrenocortical Adenoma  
Orexin A, through OXR1, promoted the release of cortisol from scattered human adrenocortical cells; orexins A/B and prepro-orexin were not located in the normal adrenal cortex. In addition, prepro-orexin mRNA and orexin A, but not orexin B, were observed in cortisol-accreting adenomas (a benign neoplasm of the adrenal cortex); these adenomas expressed OXR1/OXR2 mRNAs which were upregulated compared with the levels observedin normal adrenal cortices [80]. Orexin A, but not orexin B, increased the basal cortical release from adrenocortical adenomas and normal cells and both orexins augmented normal/adrenocortical adenoma cell proliferation [80]. Another study reported a high level of OXR1 mRNA and a low level of OXR2 mRNA in the human adrenal medulla and cortex, whereas only OXR2 has been detected in benign secreting pheochromocytomas [81]. OXR1 and OXR2 have been detected in human normal glands, adrenocortical adenomas and pheochromocytomas [86]. OXR1 was expressed in the cortex (reticular, fasciculate and glomeralosa regions) and OXR2 in the medulla (nonepinephrine and epinephrine cells) of normal glands, whereas adrenocortical adenomas (cortical tumor) expressed OXR1, but not OXR2, and pheochromocytomas (medullary tumor) only expressed OXR2 [86]. Orexins A and B also decreased basal pituitary adenylate cyclase activating polypeptide (PACAP)-induced tyrosine hydroxylase mRNA level and blocked the PACAP-induced cAMP augmentation level in the rat pheochromocytoma PC12 cell line; this means that the suppressive action on tyrosine hydroxylase mRNA was, at least in part, mediated by the cAMP/protein kinase A pathway [87].  
Orexin A decreased OXR2 expression and increased cortisol release in the NCI-H295R cell line [88,89]. Akt signaling is involved in the survival and proliferation of NCI-H295R human adrenocortical cells mediated by orexin A [89]. The peptide also increased OXR1 protein/mRNA expression, cell proliferation, 3\(\beta\)-hydroxysteroid dehydrogenase protein/mRNA expression, Akt phosphorylation and cortisol synthesis in these cells [89]. Orexins A and B promoted the release of catecholamines and increased inositol trisphosphate synthesis (not cAMP production) from pheochromocytoma slices; the latter effect was inhibited with U-73122 (a PLC blocker) and the former with U-73122 or calphostin (a PKC antagonist), but it was not blocked with SQ-22536 (an adenylate cyclase inhibitor) or H-89 (a protein kinase A blocker) [81]. This means that orexins favor catecholamine release from human pheochromocytomas via OXR2 coupled to the PLC-PKC signaling cascade. Moreover, both orexins also blocked basal and PACAP-induced dopamine release from PC12 cells; OXR2 was strongly expressed in the rat adrenal gland but not OXR1, and OXR1/OXR2 were not expressed in PC12 cells [87]. However, both orexins decreased the level of tyrosine hydroxylase in PC12 cells, suggesting that orexins exert this action through non-orexin receptors [87].
###### Abstract  
This study implemented the measurement results and administrative information obtained from the hole plate into the Digital Calibration Certificate (DCC). The DCC comprises three parts: Norms and Standards, Hierarchical Structure, and XML as Exchange Format. DCCs play a significant role in the field of metrology and statistics by ensuring data interoperability, correctness, and traceability during the conversion and transmission process. The hole plate is a length standard used for two-dimensional geometric error measurements. We evaluated the accuracy of the high-precision coordinate measuring machine (CMM) in measuring a hole plate and compared the measurement error results obtained from the hole plate with those of the laser interferometer, autocollimator, and angle square. The results show that the maximum difference in linear error is \(-0.30\)\(\upmu\)m, the maximum difference in angle error is \(-0.78\)\(\upmu\)s, and the maximum difference in squareness error is \(4.54\)\(\upmu\)s. The XML is designed for machine-readability and is modeled and edited using the XMLSpy 2022 software, which is based on information published by PTB. The administrative management and measurement results tasks are presented in PDF format, which is designed for human-readability and ease of use. Overall, we implemented the measurement results and information obtained from the hole plate into the DCC.  
DCC; CMM; XML; hole plate; geometric error Article
## 2 Method
### Damages by Local Soil Conditions in the Light of 2023 Kahramanmaras Earthquakes
When examining the ground-related damages after the Kahramanmaras earthquakes, it was observed that the damage was primarily concentrated in three areas: proximity to the fault, liquefaction, and the effects of thick sediment basins and bearing capacity problems (Figure 3). In the visuals between Figure 3a-f, undulations and collapses in the ground occurred due to the close fault effect and fracture propagation. The effects of ground liquefaction are illustrated in Figure 3g-h. An example of overturning damage caused by inadequate soil-foundation connection is shown in Figure 3i. Figure 2: Distribution of earthquakes and subsequent aftershocks on 6 February 2023. In regions such as Hatay and Golbasi (Adhyaman), settlements were made on very thick sedimentary units, and most structures were built with shallow foundations. The basin-type sedimentary formations in these areas caused significant shaking during earthquakes, leading to severe damage, primarily due to ground-related issues such as bearing capacity problems and liquefaction. In Hatay city center and its districts, multi-storey buildings sustained extensive damage, and more than 22,000 people lost their lives. In Golbasi (Adhyaman), predominantly single- or two-storey buildings remained largely unaffected, while buildings of three or more storeys suffered heavy damage. In the Kahramanmaras earthquakes, much of the damage was linked to factors like soil liquefaction, the amplification of seismic waves, and construction quality. Especially regarding the role of soil in earthquake impacts, the specific soil characteristics of the region in question (liquefaction potential, amplification effects, etc.) should be carefully considered when applying these findings to other earthquake-prone areas. Specific mitigation strategies and construction techniques are necessary to address the local geotechnical conditions and seismic hazards. As a result, considering the details of the earthquake-soil-structure triple interaction will minimize possible damage.
## 2 Materials and Methods  
### Study Design and Participants  
This study employed a prospective cohort design conducted at a single center. Twenty-four (15 females and 9 males) adolescents with idiopathic scoliosis (AIS) with a mean age of 14.5 \(\pm\) 2.3 years were included in the study. The criteria for inclusion in this study were specifically defined as follows: Firstly, participants must conform to the diagnostic criteria for scoliosis as set by the International Society of Spine Surgery [17]. Secondly, they should have double curvature (S type scoliosis) and a Cobb angle ranging between 10\({}^{\circ}\) and 20\({}^{\circ}\). Thirdly, individuals included in the study should not have undergone surgical treatment for scoliosis nor should they have plans to undergo brace treatment in the foreseeable future. Additionally, they must not have any motor organ diseases. Finally, participants must provide informed consent for this study, willingly participate, and sign the consent form. On the other hand, the exclusion criteria for this study were as follows: individuals with non-idiopathic scoliosis were excluded. Participants with growth and developmental disorders, or a history of nerve, muscle, and bone infections, psychological issues, etc., were also not included. Furthermore, individuals with obvious deformities of the lower limbs and feet, or those who had undergone major surgery in the past were not eligible for this study. The R package \"pwr\" was used to estimate the sample size ([PERSON], University of Kiel, Kiel, Germany) for a two within-factor repeated measures design. The effect sizes for the side effect (n\({}^{2}\)partial = 0.095) and the interaction effect (n\({}^{2}\)partial = 0.032) were too small, so the sample size was calculated based on exercise type (n\({}^{2}\)partial = 0.734, f = 1.081). According to the pilot study based on five subjects' values, the minimum sample size was obtained as 17 subjects with a power of 0.80, an alpha level of 0.05, and an effect size of f = 0.6 \(>\) 0.40 as large. The study was completed with a total of 24 subjects, and all subjects gave informed consent.
## 3 Experiments and Results
### Network Configurations and Training  
In Section 2, we proposed the AEUNet++ model based on the traditional UNet++. It can be regarded as an improved UNet++, so in the two experiments, the performance of the proposed AEUNet++ model was compared with the four SOTA methods, including U-Net, UNet++, SegNet and DeeplabV3+. All networks in the experiments used the same settings: adaptive moment estimation (Adam) optimizer was adopted, the initial learning rate was 0.0001, batch size was 5, and the learning rate was decreased by 0.5 times every 50 epochs. We conducted 100 epochs on the two data sets. All networks were implemented with Pytorch 1.2.0 and python 3.7.9, and were checked using a single Nvidia Tesla P100 GPU with 16 GB.  
In addition, before the training stage, it was necessary to generate the defined distance maps used in the multi-task learning module. In all experiments, as shown in Figure 5, based on the building mask in the ground truth samples, the building boundaries were extracted through the canny edge detection. Then, for each of the building boundaries, the truncated signed-distance of each pixel was calculated based on Equation (5). Finally, each building mask described by the truncated signed-distance was categorized into different classes, namely distance class maps (e.g., Figure 5d). Here, we repeated tests using AEUNet++ with different truncation thresholds by step 1 in the interval [15; 30] and the different truncated signed-distance class number by step 1 in the interval [5; 20] in the WHU data set. Figure 8 shows the intersection over union (IoU) curve obtained with different pairs of thresholds (drawing a point every 10 points); the curve fluctuates and reached the highest IoU at threshold pairs (20, 11); thus, the truncation threshold was set to 20, and the truncated signed-distance class number was set to 11 in the experiment.
## 3 Results and Discussion
### Optimal Conditions for the Expression of Recombinant Vectors in E. coli  
The pET28a (+) vector contains the T7/lacl promoter, without induction, which was silenced in _E. coli_ BL21 (DE3) host bacteria. The expression level of the reFAE was mainly affected by the inducer concentration, OD\({}_{600}\), temperature and duration. Then, the optimal conditions were explored in terms of the four aspects of induction timing OD\({}_{600}\) (0.3-1.8), inducer concentration (0.01-0.1 mmol/L), induction duration (4-24 h) and induction temperature (15-37 \({}^{\circ}\)C); one factor was changed and the others were constant. The result is shown in Figure 2. The optimal induction conditions were as follows: culture temperature of 37 \({}^{\circ}\)C, OD\({}_{600}\) of 1.2, IPTG concentration of 0.04 mmol/L, induction temperature of 20 \({}^{\circ}\)C and time of 20 h. As for reFAE2, the final IPTG concentration was 0.02 mmol/L, and other conditions were the same as reFAE1 (Figure 2).  
Figure 1: Alignment of the amino acid sequences among reFAE1, reFAE2, and other FAEs. The strictly conserved residues are demonstrated by white characters with red boxes, red characters denote the residues with high similarity in each group. The residues with high similarity across the groups are boxed in blue frames.
## 6 Evaluation
### Experimental Setup  
The simulations were performed within an Arch Linux virtual machine--that is at the time the development environment of the simulator--on a Mac Pro with a 3.5 GHz 6-Core Intel Xeon E5 CPU, 16 GB 1866 MHz DDR3 RAM, AMD FirePro D500 3072 MB GPU on MacOS Mojave. The virtual machine used 6 cores and 8192 MB RAM. The encryption service as well as the plain text aggregation resided on an external server, so that differences in the overall topology can be neglected in the comparison. It was run on a single 2.7 GHz Intel Xeon E5 Core. The simulation and the encryption service communicated through ethernet. This setup with separated simulation and encryption service also ensured that there is no computation power for the simulation lost to the encryption service.
## 4 Convolutional/LSTM-Based Resource Allocation Framework
### Training Algorithm  
Though the model is composed of two different kinds of network architectures, i.e., the Convolutional and LSTM Neural Networks, it can be jointly trained with one loss function. In order to our neural network to predict by taking account the sign of prediction error, its loss function cannot be symmetrical as in classical predictors such as Root Mean Squared Error (RMSE) or the Mean Absolute Error (MAE). We define an asymmetrical one that weighs allocation errors \(e_{h,nS+j}=c_{h,nS+j}^{a}-\hat{\mathbf{c}}_{h,nS}^{a}\) (\(h\in[1..N_{VNFI}^{a}];j\in[1..S]\)) differently. In particular a positive error corresponds to resource under-allocation and leads to QoS degradation with a cost penalty of \(C_{QoS}\) for each Gbit not allocated. Conversely negative errors lead to over-allocation problems and to higher costs of allocated capacity resources; in particular for each Gbit of over-allocated capacity the cost is given by \(C_{RA,i}^{a}\) expressed by (1) for the \(i\)-th (\(i\in[1..Q]\)) type VNFI belonging to the NFVI-PoP \(\bar{u}\in\tilde{\mathcal{U}}_{NP}\). For this reason the expression of the loss function \(J\) is the following:  
\[J=\frac{1}{\|\mathbf{P}\|}\sum_{n\in\mathbf{P}}J_{nS} \tag{8}\]  
\[J_{nS} = \frac{1}{SN_{VNFI}^{a}}\sum_{h=1}^{N_{VNFI}^{a}}\sum_{j=1}^{S}(C_ {QoS}I(\epsilon_{h,nS+j})e_{h,nS+j}\] \[- C_{R,A,h}^{(*),\bar{u}}I(-e_{h,nS+j})e_{h,nS+j})\]  
where \(J_{nS}\) denotes the average weighted error in the allocation period \(t\in[nT_{a},(n+1)T_{a})\), \(\mathbf{P}\) denotes the collection of time points when the allocations are conducted of training samples, \(\|\mathbf{P}\|\) denotes the number of training samples, \(I(x)\) is the indicator function and \(C_{R,A,h}^{(*),\bar{u}}\) depend on the VNFI type and can expressed as:  
\[C_{RA,h}^{(*),\bar{u}}=\sum_{i=1}^{Q}C_{RA,i}^{a}\alpha_{i,h}^{a}\ \ h\in[1..N_{VNFI}^{a}]\ \bar{u}\in\tilde{\mathcal{U}}_{NP} \tag{10}\]  
where \(\alpha_{i,h}^{a}\) is a binary variable assuming the value 1 if the \(h\)-th VNFI of the NFVI-PoP \(\bar{u}\in\tilde{\mathcal{U}}_{NP}\) is of \(i\)-th type; otherwise its value is zero  
Finally the training of the neural network is performed by applying the RMSprop algorithm proposed by [PERSON] and [PERSON] [32].