esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Discussion
### Performance of the Intent-Estimation Method  
To test the performance of the intent-estimation method, a comparison was conducted between our method and the method applied in [11] that utilized the motion characteristics of detected vehicles and ignores the road structure. For the straight road scenes, 350 events were used in the test, including 278 normal moving events, 38 left lane changes and 34 right lane changes. For the winding road scenes, 233 normal moving events, 38 lane changes and 29 right lane changes were used.  
The results of intent estimation on straight roads are shown in Table 3, and the results on winding roads are shown in Table 4. The tables contain precision rate, recall rate and F-score, which were usually used in information retrieval binary classification and pattern recognition. A high precision rate means that an algorithm returns substantially more relevant results than irrelevant ones, and high recall rate means that an algorithm returns most of the relevant results. The F-score is a combination of precision rate and recall rate, which can be used to represent the accuracy of an algorithm. The computation of the precision rate, recall rate and F-score is attached in Appendix B, as well as the confusion matrix of the testing results.  
On straight roads, the average F-score of the proposed method is 91.7%, and the average F-score for the method used in [11] is 76.3%. On winding roads, our method achieves an average F-score of 90.5%, and it was 53.2% for the method used in [11]. The accuracy of our method is much higher on both straight roads and winding roads compared with the method used in [11]. The difference between the accuracy of our method on the two kinds of roads is 1.2%, while it is 20.1% for the method used  
Figure 12: Testing roads. (**a**) Satellite map of testing roads which are represented with red lines and blue lines; (**b**) a straight road where red lines are road curves, white lines are lane marks and blue points represent obstacles; (**c**) a winding road.  
in [11], which indicates that our method is more robust. The experimental results illustrated that our method performs considerably better, especially on winding roads, than the method used in [11].
###### Abstract  
Laryngeal high-speed videoendoscopy (HSV) allows objective quantification of vocal fold vibratory characteristics. However, it is unknown how the analyzed sequence length affects some of the computed parameters. To examine if varying sequence lengths influence parameter calculation, 20 HSV recordings of healthy females during sustained phonation were investigated. The clinical prevalent Photron Fastcam MC2 camera with a frame rate of 4000 fps and a spatial resolution of 512 \(\times\) 256 pixels was used to collect HSV data. The glottal area waveform (GAW), describing the increase and decrease of the area between the vocal folds during phonation, was extracted. Based on the GAW, 16 perturbation parameters were computed for sequences of 5, 10, 20, 50 and 100 consecutive cycles. Statistical analysis was performed using SPSS Statistics, version 21. Only three parameters (18.8%) were statistically significantly influenced by changing sequence lengths. Of these parameters, one changed until 10 cycles were reached, one until 20 cycles were reached and one, namely _Amplitude Variability Index_ (_AVI_), changed between almost all groups of different sequence lengths. Moreover, visually observable, but not statistically significant, changes within parameters were observed. These changes were often most prominent between shorter sequence lengths. Hence, we suggest using a minimum sequence length of at least 20 cycles and discarding the parameter _AVI_.  
high-speed videoendoscopy; glottal area waveform; sequence length; parameters; diagnosis 2018 8 2666; doi:10.3390/app8122666
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 4 The Role of lncRNAs beyond Metastasis
### Immune Modulation  
LncRNAs were found to modulate the immune response in BC. [PERSON] et al. identified 11 lncRNAs, including LINC01010, LINC00668, and LINC02418, that were associated with immune cell infiltration within the tumor microenvironment. These lncRNAs influenced immune cell behavior, cytokine production, and antigen presentation, thereby affecting tumor progression and patient prognosis [23]. A low expression of another lncRNA Cell leukemia/lymphoma 6 (TCL6) was found to be associated with worse prognosis in PR-negative BC patients and served as an independent factor for poorer survival, particularly in luminal B BC, by regulating immune-related pathways and correlating with various immune infiltrating cells or immune checkpoint molecules [24]. [PERSON] et al. studied the relation between TINCR and the upregulation of PD-L1. The research revealed that TINCR upregulated USP20 expression through the dual mechanism. In the cytoplasm, TINCR functions, through the ceRNA regulatory mechanism, as a sponge for miR-199a-5p, increasing the stability of USP20 mRNA and thereby promoting the expression of PD-L1 by blocking its ubiquitination. In the nucleus, TINCR may attract DNMT1 in order to promote methylation and, as a result, suppress miR-199a-5p transcription. Decreased miR-199a-5p level weakens its inhibition of USP20 mRNA stability and consequently may lead to the upregulation of PD-L1 expression. Researchers emphasize the importance of a potential combination of TINCR knockdown and PD-L1 inhibitor as a target for BC immunotherapy [25].
# Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications
## 3 Radio Channel Characteristics and Models  
RSS measurements are mainly used in two different approaches for localization, including ranging and location fingerprinting [6; 10; 12; 13; 17]. With the ranging approach, RSS measurements are used to estimate the distance between radio transceivers based on a pre-determined radio signal path loss model. Then, the distance estimation is used to estimate location through the trilateration method. In contrast, with the location fingerprinting approach, RSS measurements are collected as location fingerprints at specific locations of interest in the offline phase. The location fingerprints are used to train a classification model, which is then used in the online phase to estimate location based on instantaneous RSS measurements. Radio channel characteristics place fundamental limits on the performance of RSS-based localization techniques. In this section, we present a brief review of radio propagation channel characteristics and discuss their impacts on the RSS measurement data.
## 3 Mathematical Model of This Study
### Decision of the Heuristic Algorithm  
Since the scheduling and route planning problems have been proven to be NP-hard problems, the study applied a genetic algorithm as the major structure and established a heuristic algorithm to obtain the approximate optimization of the problem. The genetic algorithm uses the solution of the global solution to search, instead of the other algorithm searching from a single solution in the optimal solution. The advantage of searching by the global optimal solution is that it can avoid the situation of falling into the local optimal solution. For this reason, it is a stable method that was used in many studies.  
As mentioned above, the decision-making platform is divided into two states, match and real-time. The unmanned vehicle will take in requests in the logistics center and process the request combination of the optimum delivery cost under the known factors of number of requests, delivery conditions, vehicle capacity, delivery region, and the customer's expected time window. Since we already know the customer requirements in the platform, we can process the initial routing, focusing on the known requirements in the match state; the solution computing steps are:  
1. Generate random feasible routes.
2. Process reproduction on the selected shorter routes.
3. Pair individuals, chose two nodes randomly, and check the existence of the travel route. A certain subsequent route can be reversed to achieve the result of mutation if nodes conflict.
4. Generate a new plan (offspring Routes + mutation Routes).  
However, conflicts may occur during the route planning process. These changes may result in the initial routing not being optimal and the consideration of different situations. Since the temporary requirement occurs in the delivery process in a real-time state, the total time deviation of the entire customer cluster will need to be considered; its purpose is to minimize the deviation. This also reflects that the service time in the new plan should match the service time requirement from the original plan, as much as possible. Thus, if computing a new delivery sequence is required, the steps are:  
1. Determine that the vehicle still has space for rush requests if the solution of the match state were satisfied; scheduling should be ended if the vehicle does not have the capacity for new requests.
2. Compute the algorithm to increase revenue and consider customer reception for early or late delivery.
3. Establish corresponding feasible routes for every possible backup schedule.
4. Apply the optimal cost computed in model control of the real-time state as the initial price and calculate every feasible route. If no optimal solution can be computed, recalculate again using the rush request step of the real-time state model evolution.
5. Select the best solution, with minimum operating and delay costs.  
The computing process of the heuristic algorithm is shown in Figure 4.
## 6 Security Solutions
### Service Security  
IoT systems are designed based on the producer-consumer model. In an IoT network, smart devices operate as service providers, and end users, such as the smartphone of a device owner, are considered service consumers. For instance, in a smart healthcare service, medical sensors provide services that allow physicians to monitor patients' health conditions remotely using smartphones or through a web portal. Access to these services should be protected to ensure privacy and confidentiality of the sensitive information.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Scheme** & **Interoperability** & **Resilience** & **Scalability** & **Communication** & **Computation** & **Memory** \\ \hline Certificate based [128] & \(\bigstar\bigstar\) & \(\bigstar\bigstar\) & \(\bigstar\bigstar\) & \(\bigstar\bigstar\) & \(\bigstar\) & \(\bigstar\) \\ \hline DTLS-PSK [122] & \(\bigstar\bigstar\) & \(\bigstar\) & \(\bigstar\) & \(\bigstar\bigstar\) & \(\bigstar\bigstar\) & \(\bigstar\bigstar\) \\ \hline Modified DTLS [130] & \(\bigstar\) & \(\bigstar\bigstar\bigstar\) & \(\bigstar\bigstar\) & \(\bigstar\bigstar\) & \(\bigstar\bigstar\) & \(\bigstar\bigstar\) \\ \hline Delegation based [131; 132] & \(\bigstar\bigstar\bigstar\) & \(\bigstar\) & \(\bigstar\) & \(\bigstar\bigstar\) & \(\bigstar\bigstar\) & \(\bigstar\bigstar\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: A comparison between DTLS-based schemes. A security property can have three different values: high (\(\bigstar\bigstar\bigstar\)), medium (\(\bigstar\bigstar\)), and low (\(\bigstar\)). A value is assigned to a security property based on a DTLS scheme’s performance to support that property.
## 3 Traffic Multi-Maps Generation Based on Disjoint Path Flows
### TWM Model  
A multi-map TWM, \([\mu_{m}]\), is a collection of \(M\) cost-weighted views (maps) of the traffic network \(\Theta\), which the traffic groups in the network can use. Each map \(\mu_{m}\) is a representation of the traffic network \(\Theta\) (the \(m\) view), which complements the network map \(\Theta\) with an additional set of link-weights and time constraints \(\mu_{m}=\{[\beta_{e}^{m}],T_{m}\}\).  
The Listing 1 shows the map appearance for the open source microscopic traffic simulation environment \"Simulation of Urban MObility\" (hereafter, SUMO) [70].  
```
<wm id=\"002\"  mapName=\"Madrid-LasTablas\"  format=\"sumo_1\"  version=\"1.0\"> <validity begin=\"0\" end=\"2000\" id=\"time_001\">  <edge id=\"NodeA_NodeB\" weight=\"1.871288\"/>  <edge id=\"NodeA_NodeC\" weight=\"2.508750\"/>  <edge id=\"NodeA_NodeD\" weight=\"1.918288\"/> ... <edge id=\"NodeM_Node]\" weight=\"2.470722\"/>  <edge id=\"NodeM_NodeL.\" weight=\"2.176508\"/>  </validity> <validity begin=\"2001\" end=\"4500\" id=\"time_002\">  <edge id=\"NodeA_NodeB\" weight=\"1.871288\"/>  <edge id=\"NodeA_NodeC\" weight=\"3.882523\"/>  <edge id=\"NodeA_NodeD\" weight=\"2.640266\"/> ... <edge id=\"NodeM_Node]\" weight=\"2.730023\"/>  <edge id=\"NodeM_NodeL.\" weight=\"2.176508\"/>  </validity> </wm>
```  
The TWM generation function \(\Pi\) (17) provides the \([\mu_{m}]\) network views, taking as input (a) the network topology; (b) the \(Q\) traffic groups (fleets) that use the network; (c) the existing time-constraints \([\Gamma_{m}]\) for usage; and (d) network traffic status data \(\Phi\). Time constraints \([\Gamma_{m}]\) consider both periodic-scheduled constraints (i.e., based on traffic restrictions, scholar times, commercial times, or similar) and event-based time constraints (i.e., road works, public events, road incidents, and others).  
\[\Pi:\Theta,Q,[\Gamma_{m}],\Phi\rightarrow[\mu_{m}] \tag{17}\] \[\mu_{m}=\{[\beta_{e}^{m}],\Gamma_{m}\}\]  
Some routing agents may not use the TWM mechanism due to different causes: they are not part of the traffic groups that receive TWM, the TWM is not received on time, or they decline using TWM for their trip. They use the weights \([w_{e}]\) corresponding to the original physical map. We define the adherence factor \(\psi\) (18) as the fraction of routing agents \([v_{a}]_{TWM}\) that are using it. We use \(\psi\) to compare different TWM adoption scenarios.  
\[\psi=\frac{\sum[v_{a}]_{TWM}}{\sum[v_{a}]} \tag{18}\]
## 4 Landscape of Immunotherapy for HCC  
The results of the most important clinical trials exploring the results of immunotherapies in advanced-stage HCC are reported in Table 1. Several other trials investigating the role of ICIs as an adjuvant therapy after curative treatments, or as complementary therapy to intra-arterial therapies in locally advanced disease, are currently ongoing.  
The era of immunotherapy for HCC patients started with studies evaluating the outcome of ICIs monotherapies. Based on the efficacy data of phase II trials, the FDA granted accelerated approval to the anti-PD-1 antibodies nivolumab and pembrolizumab in monotherapy for the second-line treatment of advanced stage HCC [40; 41]. However, the confirmatory phase III studies of nivolumab vs. sorafenib in the frontline (CheckMate 459) [42] and pembrolizumab vs. placebo in the second-line (KEYNOTE-240) [43] failed to meet their primary OS endpoints (although the overall response rates and the safety profile was confirmed for both drugs). Despite being unsuccessful, phase III trials with ICIs monotherapies provided important information: (1) these drugs are safe and well-tolerated even in patients with HCC, most of whom have underlying liver cirrhosis; (2) the objective response rate is 15-18%, which is significantly higher than that achievable with TKIs [42; 43]; (3) patients with an objective response have long survival [40]. In order to overcome the limitations of immunotherapy with a single agent in terms of response rate and patient survival, the combination of different drugs has been tested. In the following paragraphs, we briefly report established combination strategies (i.e., the combination of drugs tested in concluded phase III clinical trials).
# Electron Beam Characterization of REBCO-Coated Conductors at Cryogenic Conditions
## 2 Material and Methods  
### Samples Preparation  
The two investigated samples of REBCO-coated conductors came from two different manufacturers, SuNAM and SuperOx. The samples were stored in a plastic box filled with air dehumidified by silica beads. Prior to measurements, the REBCO tapes were scissor-cut into 1 cm\({}^{2}\) large pieces, glued to flag-style copper sample holders with a silver-filled epoxy, and cured to 80 \({}^{\circ}\)C under a high vacuum.  
The tapes have a layered structure deposited over a Hastelloy C276 used as a substrate, as visualized in Figure 1 on the right. There is a range of deposition methods [30], from which SuNAM uses RCE-DR (reactive co-evaporation by deposition and reaction) [31] and SuperOx uses PLD (pulsed laser deposition) [32]. Hence, the samples will be referred to as PLD-deposited and RCE-deposited to best represent the surface. The REBCO layer itself,i.e., the Gd-Ba-Cu-O, was deposited with 1600 nm and 900 nm thickness for RCE and PLD, respectively, and was grown on buffer underlayers [27]. Moreover, these studied samples were not the typical off-the-shelf REBCO tapes because they lacked the micrometer-sized protective overlayers typically present on industry-grade tapes. Indeed, this is done because the application in RF requires the HTS coating to be exposed, minding the small micrometer-sized skin depth. The RCE-deposited sample had its usual Ag overlayer removed by the manufacturer so effectively that it lacked Ag traces in the XPS spectrum, unlike the PLD-deposited sample. The protective micrometer-thick Ag overlayer had to be etched away from the PLD-deposited sample using a solution of NH\({}_{3}\):H\({}_{2}\)O\({}_{2}\):CH\({}_{3}\)OH in 1:1:5 proportions, as detailed in [28], which inevitably affected its surface chemical state.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 1 Introduction
### Structure of the Paper  
In Section 2, the signal model is presented. Then, in Section 3, the developed measurement system is described. We describe the time-division multiple access (TDMA) structure and describe why an automatic gain control (AGC) is implemented. In Section 4, the need for system calibration is discussed as is how the sounder was calibrated in the presented measurement campaign. The measurement campaign is described in Section 5, along with the environment and the channel sounder configuration. The results are presented in Section 6, including an analysis and discussion of both the communication aspect and sensing possibilities of distributed MIMO. Finally, our conclusions and future work are outlined in Section 7.
## 4 Experimental Results and Analysis
### Part Identification Experiment  
#### 4.2.1 Two-Dimensional Code Positioning and Structure Extraction Experiments  
For QR code decoding experiments, the camera shoots some randomly placed sheet metal parts. This subsection runs tests on low-contrast OR code images to ensure the stability of the QR code decoding framework. Figure 13 depicts the images produced by each stage of the QR code localization algorithm, from coarse to fine localization. As seen, this method is capable of effectively localizing the low-contrast encoding region.  
Figure 14 depicts the waveforms of the QR code contour's horizontal and vertical projections. Backward differencing the projection values in the horizontal and vertical directions yields the projection peak, which is used to grid segment the target area. Figure 15a depicts the QR code grid segmentation result. As shown in Figure 15b, the structure of the code is obtained based on the distribution matrix of the gray mean of the segmented QR code, and the structure is clearer when compared with the original QR code image. The Zbar algorithm can quickly determine the part's type.  
Figure 14: Waveform diagram of the projection of the QR code contours in the horizontal and vertical directions.  
Figure 13: Images depicting the QR code localization process: (**a**) image of a morphological closed operation, (**b**) result of coarse localization, (**c**) image of edge detection by a Canny operator, (**d**) image showing the process of locating three position detection patterns, (**e**) image of precise localization, and (**f**) result of localization.
## 3 Methodology
### Data Pre-Processing
#### 3.3.4 Data Generation  
To ensure the robustness and versatility of our CNN-ResBiGRU model, we employed a rigorous 5-fold cross-validation approach for training and assessment. This widely accepted method for evaluating a model's performance involves dividing the dataset into five equal-sized subsets, or folds, and iteratively using each fold as a validation set while training the model on the other four-folds.  
The Myogym dataset, which consists of sensor data from 10 subjects doing 30 different gymnasium activities, was divided into five folds using random partitioning in our analysis. Each fold included around 20% of the sample set, guaranteeing an equitable data distribution across all folds. The CNN-ResBiGRU model was then trained and evaluated five times, with a different fold being used as the validation set in each iteration.  
During each iteration of the 5-fold cross-validation procedure, the model was trained using 80% of the data (four folds) and validated with the remaining 20% (one fold). This pro  
Figure 4: A visualization of the sliding window segmentation with a fixed-width window size of 13 and an overlapping proportion.  
cess was repeated five times, with each fold serving as the validation set exactly once. The final performance measures, such as accuracy and F1-score, were computed by averaging the results from the five iterations, providing a reliable estimate of the model's performance.  
The 5-fold cross-validation approach is essential because it guarantees that all Myogym dataset samples are used for training and validation. This helps to reduce the danger of overfitting and provides a more accurate evaluation of the model's capacity to generalize.
## 2 Experimental Section
### Instrumentation and Synthesis
#### 2.2.5 1,3-Difluoro-4-(Nitroprop-1-Enyl)Benzene **12**  
43% yield mp. 48-49 \({}^{\circ}\)C. \({}^{1}\)H NMR (300 MHz, CDCl\({}_{3}\)): \(\delta_{\rm H}\) (ppm): 8.06 (s, 1H, C=C-H), 7.45-7.35 (dd, \(J\) = 8.30, 8.38 Hz, coupling due to two fluorine atoms were coupling with this proton. 1H, Ar-H); 7.04-6.96 (dd, for the proton at the middle of two fluorine atoms it should split to a quartet as two fluorine atoms were coupling with it. \(J\) = 8.47, 8.50 Hz. 1H Ar-H. The other proton at position 6 should be a triplet as there is only fluorine atom coupling with it. t, \(J\) = 8.48 Hz, 1H, Ar-H), 2.38 (s 3H, CH\({}_{3}\), \(E\)), 1.57 (s 3H, CH\({}_{3}\), \(Z\)). \({}^{13}\)C NMR (300 MHz, CDCl\({}_{3}\)) \(\delta_{\rm C}\) (ppm): 163-162.3 (quartet due to two fluorine atoms coupling with the carbon. \(J\) = 222.02, 233.55 Hz. 1C, F-C=C-C-F), 162.2-159 (q, \(J\) = 234,37 Hz. 1C, F-C=C-C-F), 149.5 (s, C=C \(\alpha\) carbon), 134.1-131.1 (q, \(J\) = 234.64 Hz, 1C, F-C-C=C-C-F), 125.7-125.4 (s, C=C, \(\beta\) carbon), 117.0-116.5 (d, \(J\) = 9.88 Hz, 1C, F-C=C), 112.3-111.7 (q, \(J\) = 3.57, 21.68, 24.97 Hz, 1C, F-C=C-F), 104.8 - 104.4 (d, \(J\) = 25.80, 1C, F-C-C=C), 14.2-14.0 (CH\({}_{3}\)). GC/MS \(m\)/z (M\({}^{+}\)) 199.